The Leading Edge Septiembre 2012.pdf

September Vol. 31, June 2012, 2012, Vol. 31, No. 6 No. 9

Special Section:

Geophysics in reserves estimation

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Table of Contents

1016......  Meter Reader—Pioneering water-bottom gravity: Jack Weyand’s amazing life and career— Part 2, P. Millegan and J. Bain Special section: Geophysics in reserves estimation 1024......  An introduction to this special section: Geophysics in reserves estimation, R. Lorenzen, H. Kloosterman, and W. Abriel 1028......  Introduction to the Petroleum Resources Management System and the implications for the geophysical community, R. Lorenzen, S. Purewal, and J. Etherington 1034......  The role of geophysics in petroleum resources estimation and classification—new industry guidance and best practices, H. Kloosterman and P.-L. Pichon 1042......  Resource assessment based on 4D seismic and inversion at Ringhorne Field, Norwegian North Sea, D. Johnston and B. Laugier 1050......  Seismic technology supporting reserves determinations: Gorgon Field, Australia, R. van der Weiden, P. Nayak, and P. Swinburn 1060......  DHI support for resources evaluation: Confidence assessment examples, P.-L. Pichon, S. Delahaye, G. Fabre, and P. Desegaulx 1066......  Relating seismic interpretation to reserve/resource calculations: Insights from a DHI consortium, R. Roden, M. Forrest, and R. Holeywell Stochastic volume estimation and connectivity analysis at the Mallik gas hydrate field, 1076......  Northwest Territories, Canada, C. Dubreuil-Boisclair, E. Gloaguen, G. Bellefleur, and D. Marcotte Case study: Using seismic inversion to constrain "proved area" definition, K. Eastley, P. Unstead, 1082......  and H. Kloosterman Strategies in geophysics for estimation of unconventional resources, E. von Lunen, S. Jensen, 1090......  and J. Leslie-Panek

Departments
1002.....Editorial Calendar 1004.....President’s Page 1006.....Foundation News 1010.....From the Other Side 1012.....SEAM Update 1022.....Signals 1098.....Sections/Associated Societies Focus 1100.....Student Zone 1104.....Reviews 1108.....Announcements 1110.....Calendar 1114.....Membership 1118.....Advertising Index 1120.....State of the Net

Cover design by Kathy Gamble. Images provided by H. Kloosterman.

© 2012 Schlumberger 12-IS-0468

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The Leading Edge

September 2012

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2011–2012 SEG Executive Committee

President

The Leading Edge Editorial Board

Bob A. Hardage

Bureau of Economic Geology University Station, Box X Austin, TX 78713, USA Ph: 1-512-471-0300 Fax: 1-512-471-0140 [email protected] President-elect

Alan Jackson
Shell International E&P 3737 Bellaire Blvd. Houston, TX 77001, USA Ph: 1-713-245-8389 [email protected]

Chairman

David J. Monk

Gregory S. Baker

Apache Corporation 2000 Post Oak Blvd. Houston, TX 77056, USA Ph: 1-713-296-6339 [email protected]

University of Tennessee 1412 Circle Drive Knoxville, TN 37996, USA Ph: 1-865-974-6003 Fax: 1-865-974-2368 [email protected]

The Leading Edge® (Print ISSN 1070-485X; Online ISSN 1938-3789) is published monthly by the Society of Exploration Geophysicists, 8801 S. Yale Ave., Tulsa, Oklahoma 74137 USA; phone 1-918-497-5500. Per­ iodicals postage paid at Tulsa and additional mailing offices.
POSTMASTER: Send changes of address to The Leading Edge Box 702740, Tulsa, OK 74170-2740 USA

First vice president

William L. Abriel

William Goodway

Chevron 6001 Bollinger Canyon Road San Ramon, CA 94583, USA Ph: 1-925-842-3423 [email protected]

Apache Canada 9 Avenue Southwest Calgary, AB T2P 3V4 Canada Ph: (403) 303-5958 [email protected]

Second vice president

Richard D. Miller

Shuki Ronen

Kansas Geological Survey Campus West, 1930 Constant Ave. Lawrence, KS 66047-3726, USA Ph: 1-785-864-2091 [email protected]

CGGVeritas [email protected]

Vice president

Wafik B. Beydoun

Tad Smith

TOTAL E&P R&T USA, LLC 1201 Louisiana St., Suite 1800 Houston, TX 77002, USA Ph: 1-713-647-3504 [email protected]

Apache Corporation 2000 Post Oak Blvd. Suite 100 Houston, TX 77056, USA Ph: 1-713-296-6251 [email protected]

Secretary-treasurer

Nancy J. House

Carlos Torres-Verdin

Chevron USA 1550 Coraopolis Heights Road Moon Township, PA 15108, USA Ph: 1-412-865-3156 [email protected]

University of Texas Department of Petroleum and Geosystems Engineering 1 University Station, Mail Stop C0300 Austin, TX 78712-0228, USA Ph: 1-512-471-4216 Fax: 1-512-471-4900 [email protected] Special editor

Editor

Tamas Nemeth

Chevron 6001 Bollinger Canyon Road San Ramon, CA 94583, USA Ph: 1-925-842-0998 [email protected]

Christopher Liner

University of Houston 312 Science and Res. Bldg. 1 Houston, TX 77204, USA Ph: 1-713-743-9119 Fax: 1-713-748-7906 [email protected]

The SEG editor is an ex-officio member of The Leading Edge Editorial Board. The Leading Edge digital edition is available at: http://www.seg.org/resources/publications/tle-digital-edition.

Steven Davis, SEG executive director; Ted Bakamjian, director, publications; Dean Clark, editor; jenny kucera, associate editor; SPRING HARRIS, assistant editor; KATHY GAMBLE, graphic design manager; ROBERT L. MILLER, graphic production designer; Merrily Sanzalone, manuscript tracking specialist. Advertising information and rates: Mel buckner, phone 1-918-497-5524. Editorial information: phone 1-918-497-5535; fax 1-918-497-5557; e-mail [email protected]. Subscription information: e-mail [email protected].
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Print subscriptions for members of the Society in good standing are included in membership dues paid at the World Bank III and IV rate. Dues for Active and Associate members for 2011 vary depending on the three-tiered dues structure based on World Bank classification of the member’s country of citizenship or primary work residence. Dues are US$90 (World Bank IV countries), $48 (World Bank III countries), and $12 (World Bank I and II countries). Dues for all Student members regardless of country of citizenship or primary residence are $21 and include online access to journals. Students may receive TLE in print by paying an additional $36. Print and online single-site subscriptions for academic institutions, public libraries, and nonmembers are as follows: $155, Domestic (United States and its possessions); $190, Surface Freight (Canada, Mexico, Central and South America, Caribbean); and $200, Mandatory Air Freight (Europe, Asia, Middle East, Africa, and Oceania). For corporations and government agencies, print and online single-site subscriptions are: $840, Domestic (United States and its possessions); $875, Surface Freight (Canada, Mexico, Central and South America, Caribbean); and $885, Mandatory Air Freight (Europe, Asia, Middle East, Africa, and Oceania). Print-only subscriptions for corporations and government agencies are: $340, Domestic (United States and its possessions); $375, Surface Freight (Canada, Mexico, Central and South America, Caribbean); and $385, Mandatory Air Freight (Europe, Asia, Middle East, Africa, and Oceania). Rates are subject to change without notice. Subscriptions to the SEG Digital Library include subscriptions to TLE. Subscribers to GeoScienceWorld are entitled to a $30 discount off printonly subscriptions to TLE. See www.seg.org/publications/ subscriptions for ordering information and details. Singlecopy price is $16 for members and $32 for nonmembers. Postage rates are available from the SEG business office. Advertising rates will be furnished upon request. No advertisement will be accepted for products or services that cannot be demonstrated to be based on accepted principles of the physical sciences. Statements of fact and opinion are made on the responsibility of the authors and advertisers alone and do not imply an opinion on the part of the officers or members of SEG. Unsolicited manuscripts and materials will not be returned unless accompanied by a self-addressed, stamped envelope. Copyright 2011 by the Society of Exploration Geophysicists. Material may not be reproduced without written permission. Printed in USA.

Editorial Calendar

Issue.... Special Section theme......................................... Due date.............. Guest editors 2012 Oct.......... Marine acquisition and onboard processing, quality control......past due......................Alan Jackson*, [email protected] ........................................................................................................................................... Shuki Ronen*, [email protected] Nov......... Passive seismic and microseismic..........................................past due.................... Julie Shemeta, [email protected] ........................................................................................................................................... William Goodway*, [email protected] ........................................................................................................................................... Mark Willis, [email protected] Dec.........Passive seismic and microseismic..........................................past due.................... Julie Shemeta, [email protected] ........................................................................................................................................... William Goodway*, [email protected] ........................................................................................................................................... Mark Willis, [email protected] 2013 Jan.........Applications and challenges in shear-wave exploration.......15 Sep 2012.............. James Gaiser, [email protected] ........................................................................................................................................... Rishi Bansal, [email protected] Feb.........Chronostatigraphy...............................................................15 Oct 2012............... Alan Jackson*, [email protected] ........................................................................................................................................... Tracy Stark, [email protected], ........................................................................................................................................... Hongliu Zeng, [email protected] Mar........Urban geophysics................................................................15 Nov 2012.............. Rick Miller, [email protected] ........................................................................................................................................... Greg Baker*, [email protected] Apr.........Marine and offshore technology..........................................15 Dec 2012.............. Shuki Ronen*, [email protected] ........................................................................................................................................... Phil Fontana, [email protected] May........Arctic / ATC..........................................................................15 Jan 2013............... William Goodway*, [email protected] Jun.........Nonreflection seismic and inversion of ..............................15 Feb 2013............... Julie Shemeta, [email protected] ..............surface and guided waves..................................................................................... Gerhard Pratt, [email protected] Jul..........Unconventional resources technology.................................15 Mar 2013.............. Chris Liner, [email protected] Aug........Hyrdrogeophysics................................................................15 Apr 2013............... Greg Baker*, [email protected] Sep.........Gravity and potential fields .................................................15 May 2013.............. Michal Ruder, [email protected] ........................................................................................................................................... Robert Pawlowski, [email protected] ........................................................................................................................................... (* Current TLE Board member)

Notice to authors TLE publishes articles on all areas of applied geophysics and disciplines which impact it. To submit a paper for possible publication in a specific issue, please e-mail an inquiry to the appropriate guest editor for that issue. Authors are encouraged to submit their papers at any time, regardless of whether they fit the schedule. To submit an article on an unscheduled topic, contact Dean Clark, TLE editor, [email protected] or 1-918-497-5535. Electronic submission of articles Electronic submissions should include the manuscript file, figures and other graphics, a PDF of the manuscript and figures, and the author’s contact information. These files can be uploaded to an FTP site (the preferred method) or burned to a CD and mailed to the appropriate editor. Once accepted for TLE, the files will be opened and edited on a Mac or a PC using various software applications. To simplify conversion, figures should be submitted in TIFF, PDF or EPS (.tif, .pdf or .eps) file formats, with a resolution of at least 300 dpi (pixels per inch). High-resolution images can be placed in Word or PowerPoint if placed large on the page; these will be converted to PDF format. Once the paper is accepted, please also mail the appropriate editor a printed color copy of the manuscript with any figures, tables, and equations to be included. For assistance with electronic submission, contact Tonia Payne, [email protected] or 1-918-497-5575. More details are online at www.seg.org/publications/tle/tle_file_prep.shtml. Notice to lead authors Lead authors of articles published in TLE who are not members of SEG should apply for a one-year free membership and subscription to TLE by contacting Member Services, fax 1-918-497-5557 or [email protected]. Lead or corresponding authors also are required to sign a copyright transfer agreement, which gives TLE permission to publish the work and details the magazine’s and the authors’ rights. TLE staff will send a form to be signed and sent back after the article is accepted for publication. The form can be downloaded at www.seg.org/ publications/tle/copyright.shtml.

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Salt Flank & Fault Mapping
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Society of Exploration Geophysicists

President’s Page Geophysics and reserves estimation

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y previous contribution to the President’s Page seems so recent (May 2012) that I have the impression I’m monopolizing your attention. Reserves estimation, the theme of the special section in this issue of TLE, is so farreaching that I would like to address this topic here as a short foreword to the introduction by R. Lorenzen, H. J. Kloosterman, and W. Abriel. Reliably estimating hydrocarbon reserves is of paramount importance not only to O&G companies, but also for regulators, investors, governments, and consumers. Such

You will find in this issue excellent casestudy examples of what and how geophysical technologies are used for evaluating resource volumes.
estimates are used in a variety of ways, from valuing/benchmarking companies to providing an outlook for the world’s energy supply (including of course the much-debated issue of peak oil). Yet, as you will read in the articles in this issue, the process for estimating reserves is complex and evolving. Scientific methodologies and expert interpretations are required to harness these estimations and mitigate associated uncertainties. Furthermore, with the stakeholders’ footprint being so wide (involving international financial institutions, regulatory bodies, reporting entities, governments, companies, etc.) and often encompassing different interests, you can imagine the motivation for all of them to define and use their own evaluation systems—making standardizing, benchmarking, and global comparisons difficult. The importance of having a universal standard for petroleum resources, with common definitions and classifications by credible organizations, was first recognized in 1937 by the American Petroleum Institute (API). The Society of Petroleum Engineers (SPE) took the lead in 1962 and released updates on petroleum reserves definitions in 1965, 1981, and 1987. SPE, in cooperation with other professional societies, released additional updates in 1997, 2000, and 2007. The latest release (222 pages), in November 2011, is “Guidelines for Application of the Petroleum Resources Management System (PRMS)” which can be found at http:// www.spe.org/industry/docs/PRMS_Guidelines_Nov2011.pdf. This release was also sponsored by the American Association of Petroleum Geologists (AAPG), the World Petroleum Council (WPC), the Society of Petroleum Evaluation Engineers (SPEE), and (for the first time) SEG. SEG’s leadership, recognizing that reserves estimation was a new and important field for our science, formed an ad hoc Oil & Gas Reserves Committee (OGRC) in 2008. Its objective: to promote the responsible use of geophysical technologies
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in the estimation of oil and gas reserves and resources. We can proudly say that, after three years of activity, OGRC has enabled the geophysical discipline to be an explicit, visible, and integral part of an international process for reserves estimation. As we all know, predictions from geophysical data are inherently uncertain. And for geophysics to use its full potential for reserves evaluation, these uncertainties need to be quantified. This has never been an easy task for geophysicists, because there are many sources of uncertainties. They come first from the measurements (e.g., acquisition parameters/footprint, data quality, bandwidth, frequency content, signal-to-noise ratio). It continues with uncertainties in the processing phase (e.g., explicit or implicit assumptions for the data and model, overburden compensation). The last source of uncertainty arises from the interpretation and calibration phases (e.g., uncertainty in tying to the well data/ logs/rock-fluid/reservoir properties, interpretation bias). Some techniques exist to quantify these uncertainties for reserves evaluation, but this is an area of research where much progress is still needed (and expected). You will find in this issue excellent case-study examples of what and how geophysical technologies are used for evaluating resource volumes. Just a few of these geophysical technologies include: 2D, 3D, 4D, AVO, DHI, WAZ, PSDM, seismic inversion, microseismic, and multicomponent. Data acquired from these technologies are then processed, interpreted, and calibrated to predict (some with uncertainty measures) the following information in reserves estimation:
1) Mapping the structure, faults, and geometry of the hydro2) Characterizing rock and fluid properties 3) Highlighting and monitoring changes in the distribution

carbon trap

of fluids and/or pressure variations resulting from production 4) For unconventional resources: Identifying the fracture network, the stimulated rock volume (SRV), and the drainage network

In conclusion, if you are interested in reserves estimation and want to get involved in this new field in geophysics, then request to join SEG’s Oil & Gas Reserves Committee (OGRC). You will not regret it! —Wafik Beydoun Vice president

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The Leading Edge

Foundation News
Geoscientists Without Borders announces two new projects

S

ince its inception in 2008, Geoscientists Without Borders (GWB) has changed lives around the world—all while positively impacting the global reputation of geophysics. Each year, the program receives dozens of applications from project managers who want to make a lasting, humanitarian impact. With 12 projects to date (some completed, others in the field), the SEG Foundation Board of Directors recently approved two new projects to join the GWB ranks. The first project award is to the University of Houston (USA) for a two-year project in Leogane, Haiti, titled “The Haiti subsurface imaging project: Helping build Haiti’s geoscience capability and searching for the 2010 earthquake fault.” Utilizing gravity, GPS, and multicomponent land and marine seismic methods, project members hope to build Haiti’s geoscience capability while working to illuminate the mystery of its devastating 12 January 2010 earthquake. Haiti’s human and technical needs are enormous, and field geophysics is an ideal way to introduce students to advanced technology while striving to mitigate real problems. Haiti’s subsurface structure and associated hazards are not well understood. With Haitian colleagues, an international team of geoscientists, and groups of students, the project will undertake a number of geophysical surveys with the goal of finding and understanding

the blind faults that are thought to have given rise to the 2010 event. Project members will use a number of geophysical techniques to image the earthquake’s epicentral area (LeoganePetit Gouve), and seek support to acquire geophysical equipment that will be used to train. The equipment, in turn, will be donated to the Haitian geoscience colleagues. Geophysically characterizing the near-surface sediment properties and searching for the proposed blind Leogane fault will assist in developing Haitian technical skills, the tectonic understanding of Hispaniola, and hazard mitigation. “The 2010 Haiti earthquake caused enormous human and infrastructural loss. Along with many, we were deeply moved by the devastation in Haiti and highly motivated to help somehow. Exploration geophysics and GWB provided the pathway,” said project manager Rob Stewart. “We have begun to assist in Haiti on the individual as well as technical fronts—by helping to build Haiti’s geophysical surveying, analysis, and educational capabilities as well as trying to image the ‘blind fault’ itself that gave rise to the 2010 earthquake. We have established the beginnings of

The Masaya volcano in Nicaragua.
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a solid working relationship with Haitian geoscience and logistics personnel. Our reconnoiter seismic and gravity surveys in January 2012 acquired good data and gave us (Haitian geoscientists with University of Houston faculty and graduate students) operational experience in the Leogane area of the fault. There are excellent opportunities for helping build technical capacity in Haiti, further assisting in the development of local personnel, and providing an intensely useful experience for an international group of students. We are excited about contributing to the understanding of this major tectonic area and building geophysics in Haiti. Exploration geophysics has mainly developed to discover and recover resources and thus, increase prosperity … noble pursuits. However, with much of the world heavily populated and reliant on sophisticated infrastructure, that prosperity can be severely compromised by natural forces. Many of our exploration techniques and organizations can be brought to bear on these hazard-related and broad scientific problems. Our geophysical methods will ultimately provide enormous social benefits, similar their economic ones. GWB focuses us on that future.” Along with the University of Houston, project partners include Southern Methodist University (USA); University of Calgary (Canada); State University of Haiti (Haiti); and Bureau des Mines et de L’Energie d’Haiti (Haiti). The second new project, awarded to The Open University (UK), focuses on volcanic activity in Nicaragua. The Open University will conduct a two-year project (“Developing integrated volcano monitoring and hazard mitigation programs at persistently degassing volcanoes”) in Masaya and Telica by utilizing gravity surveys and data integration to understand and mitigate the impact on the local environment and population of the persistent volcanic activity in those areas. Using geochemical analy-

Survey team members from the University of Houston, Dr. Paul Mann with graduate students Nathan Babcock and Li Chang, review maps and logistics in Port-au-Prince, Haiti.

ses and social science, project members hope to develop a better understanding of how persistent gas flux from the volcanos impact humans and their environment (e.g., grazing land for cattle, cultivated land for various crops, and natural vegetation) and to determine how rapidly the local environment responds to changes in volatile flux. The project will expand the scope of the research currently undertaken by the project principal investigators to include continuous gravity monitoring, enabling them to investigate processes within the shallow plumbing system of the volcano and consequently link these to variations in gas flux, sulfur deposition rates, and ground water quality. The results can subsequently be

SEG Foundation announces GSA as first professional society to become GWB supporter
SEG Foundation recently gained its first professional society supporter of the flagship Geoscientists Without Borders (GWB) program when The Geological Society of America’s (GSA) Council approved the partnership Memorandum of Agreement (MOA) at its recent meeting in Boulder, Colorado. The agreement was signed by both GSA and the SEG Foundation in June. “We are particularly pleased to welcome GSA to our GWB community. The GSA is an outstanding organization with a wide-ranging membership of enthusiastic geoscientists. Their community is warmly received,” said SEG Foundation Chair Tom Smith. As a Geoscientists Without Borders society supporter, GSA will promote GWB in its publications and Web site; provide space and a special session at the GSA Annual Meeting; and also assist the SEG Foundation in finding potential investors in the GWB program. In turn, the SEG Foundation will recognize GSA at the SEG Annual Meeting and also in The Leading Edge when GWB is featured. Additionally, GSA and the SEG Foundation will collaborate on publicity for GWB. “The Geological Society of America is pleased to have the opportunity to act as an official supporter of the Geoscientists Without Borders humanitarian program. This is an excellent program and we hope that it will continue to grow and further extend its tremendous efforts around the world,” said GSA President John Geissman of the partnership. Geoscientists Without Borders is changing lives around the world. Four years ago, the program did not exist … but today, 12 projects have been completed or are in the field and each of these projects is not only making a direct impact in those countries, but also impacting the reputation of geophysics around the world. The mission of GWB is to support humanitarian applications of geoscience around the world. “We are building the bridge to GSA. This is the first step, but this is how every voyage starts,” said Geoscientists Without Borders Chair Roel Snieder. For more information, please visit www. seg.org/gwb.
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Curious villagers in the Leogane Delta, Haiti learn the fundamentals of total station surveying from project manager Rob Stewart.

used in the development of an innovative integrated monitoring system and mitigation strategies. Additionally, the project will take a holistic approach to volcanic hazards and communicating risk mitigation strategies by involving the local community in the data collection and response processes, resulting in those most affected by the processes having a stake in data collection dissemination and response to the results. Along with The Open University (United Kingdom), project partners include the Earthwatch Institute; Society in Science (Switzerland); and Natural Environment Research Council, (United Kingdom). “GWB is one of the most successful programs that SEG has ever developed because it partners young geoscientists, applica-

tion of geophysical technology and missions of great humanitarian benefit. Who can resist? Interestingly, it is also one of the most promising programs as well. We at the SEG Foundation are honored to be the conduit to fund this effort and committed to its’ far greater future,” said Tom Smith, SEGF Board Chair. Geoscientists Without Borders was initially funded by visionary corporations. However, as more projects join the list, and to ensure that GWB continues its outstanding work, Debra and Mark Gregg, KiwiEnergy, Ltd., founded the Geoscientists Without Borders Endowment in December 2010, with their first challenge to the SEG Foundation: “raise US $125,000 and we will match it.” Now into their fourth challenge grant, Debra’s and Mark’s inspirational commitment provides matching funds for gifts to the endowment of $500 to $25,000. Only $62,652 more is needed to reach our initial goal of raising $1 million for this endowment! We need you to join us in helping GWB achieve its potential to transform lives and raise awareness of geophysics! Donate now at www.seg.org/donate and indicate “GWB Endowment” in the memo field. For more information, visit www.seg.org/gwb or e-mail [email protected]. Thank you for support! —Natalie Blythe Communications Specialist
Geoscientists Without Borders is a registered trademark of the SEG Foundation.

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The Leading Edge

From the other side
A column by Lee Lawyer with stories about geophysics and geophysicists

note from one of my “oldest” readers, Morris Gillett who is responding to FTOS in the June 2012 issue of TLE:
Well Lee, you’ve done it again ... stirred the old man’s memory. As I read your hints, I knew who the outstanding person was. First I thought of Mr. Karcher ... then you said it wasn’t. Since he was a pioneer, I knew it was Mr. DeGolyer ... wrong again. Then it had to be Mr. McCollum ... we used to talk about his work so often. Still I couldn’t come up with the answer. But I am proud to say that I met both DeGolyer and McCollum while working for The Atlantic Refining Company The memory of McCollum’s work in weight drop is particularly vivid because I was party chief of an experimental weight drop crew in the mid-fifties. My head is hanging low because I would have never thought of Scott Petty, but now I can agree that he was truly an outstanding pioneer in geophysical exploration. If I may ramble on for a minute, was it McCollum who tried the “air shot” back in the late forties or early fifties? It was when crew after crew was using pattern shot points and geophone patterns. Someone (McCollum? I’m not sure) proposed a shot pattern with small charges on poles about 8–10 ft off the ground. The idea was that the wave striking the ground would not violate Hooke’s law and might not generate as much surface interference. Although the idea was completely impractical, it indicates how desperate we were to come up with anything that would improve our data. At long last, the solution came with CDP. Until you wrote about it, I didn’t recall that Petty Geophysical was daddy of that.

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apparently focused on Brownwood. There was a lot of glass breakage, but no one was seriously injured. My memory doesn’t come up with the company that took that shot. It was a widely told story. Tell me who did that work? Now for the really interesting part of Morris’ note. “Although the idea was completely impractical …” Wow! The Poulter method was standard operating procedure in the Rocky Mountains for years. I don’t know whether it is still used. The large explosions didn’t seem to bother the wildlife that much. I guess they sounded a lot like thunder to a deer. _________________________________________ Two months ago, I wrote about tsunamis. I ventured to suggest that to get a tsunami from an undersea earthquake one would need a sizable displacement in the sea bottom. I heard from one of my many “adoring” fans. Don Plouff suggested that I was “all wet” when I suggested that a tsunami is most likely triggered by a major displacement in the sea floor. I thought about this for a fortnight or so and responded with logical argument. A day later, Don suggested that I should discuss earthquakes, so he could tell me I was “all shook up”! Okay, okay, I get it. It takes a while but I get it. All of this discussion about tsunamis was triggered (not by an earthquake) but by an article I read stating that a tsunami early warning system was proposed for the Caribbean. I pondered whether the Gulf of Mexico was part of the Caribbean. Tectonically it isn’t, but I am not sure the nomenclature was based on plate tectonics. I also received a note from Craig Beasley. Craig reminded me of a GWB project (Geoscientists Without Borders) that was investigating a major fault system that threatened Jamaica Bay and the airport just off shore. I looked up the article and conferred with Rhonda Jacobs, the SEG staff member with primary responsibility for GWB since the program began. That project was shared between the University of West Indies (Lyndon Brown) and the University of Texas-Austin (Matt Hornbach, now at Southern Methodist University). Rhonda also told me of an “extension” of that project to Haiti. The project coordinator is Rob Stewart with the University of Houston. This will tie the fault systems in Haiti to Jamaica. I checked this out on Google Earth. My, my … There is a significant trench just north of Haiti and Jamaica and bordering on the south end of Cuba called the Cayman Trench. Trenches, island arcs, and the like are active locations for earth movement and subsequent tsunamis. For those of you with money sitting around doing nothing, GWB is a good place to put it. The Foundation is trying to put together a substantial endowment fund to insure funding for GWB in the future.
Editor’s note: Geoscientists Without Borders is a registered trademark of the SEG Foundation.

I did not know who first used the “Poulter method” in seismic exploration. So I did some research and found an article in Geophysics (1950) by Thomas C. Poulter, who at that time was at Stanford Research Institute. The title is “The Poulter seismic method of geophysical exploration.” I guess he named the method after himself. He states in the article that the beginning was on the Second Byrd Antarctic Expedition (1947). They used it on the Ross Ice Shelf, and the technique was refined and further developed at the Armour Research Foundation and the Institute of Inventive Research. I had never heard of the last two organizations so back to the internet. The Armour Research Foundation has morphed into the Illinois Institute of Technology in Chicago. That is an interesting story. Look it up. Armour, as in Armour meat packing, etc., started it all with a US $1 million donation. Back to the Institute of Inventive Research, which does not show up on the Internet? If anyone has heard of this institute, please let me know. I seem to recall an incident near Brownwood, Texas. A Poulter shot was taken near the town. The explosion was To contact the “Other Side,” write to L. C. (Lee) Lawyer, Box 441449, Houston, TX 77244-1449 (e-mail LLAWYER@ prodigy.net).
1010 The Leading Edge September 2012

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The Leading Edge

SEAM Update
Phase I—RPSEA update: Controlled-source electromagnetic simulations

S

EAM Phase I RPSEA is in the process of completing a suite of geophysical simulations on the geophysical model representative of a salt area of the deep-water Gulf of Mexico. Part of this interdisciplinary effort includes the simulation of two types of electromagnetic acquisitions over the SEAM model, one for a controlled-source electromagnetic (CSEM) and one for magnetotelluric (MT). Both CSEM and MT 3D modeling and imaging technologies are of intense interest to companies conducting exploration and reservoir development in marine environments. SEAM’s goal is to provide 3D electromagnetic data that not only mimic current field acquisitions but, in most cases, far exceed them. The resulting simulation data can be used to validate

and ultimately improve 3D EM forward modeling as well as subsurface imaging and characterization methods using electromagnetic data alone or when combined with other types of geophysical data. The generated 3D CSEM data set for a wide spectrum of frequencies will also be perfectly suitable for testing new integrated interpretation workflows designed for detecting hydrocarbon reservoirs in the presence of complex structural interfaces and bathymetry. Special attention should be given to approaches that allow overcoming the masking effect on the electromagnetic responses from the salt bodies. The large number of receivers in the SEAM data set compared to current field acquisitions allows alternative acquisition geometries to be evaluated and techniques for optimization of CSEM survey design to be tested. The CSEM acquisition over the SEAM resistivity model has been completed and the resulting data have been distributed to SEAM participants. Simulations were conducted under contract from SEAM to EMGS. SEAM resistivity model The SEAM resistivity model has vertical transverse isotropic (VTI) anisotropy in the background, while a large salt body is isotropic and homogeneous with resistivity 100 Ωm. The thickness of the salt is up to 5 km. The model also has 15 anisotropic resistive anomalies (reservoirs). To perform simulations, two separate models containing the horizontal (R h) and vertical (Rv) resistivities were used. Resistivities in the horizontal model range from 0.3 to 392 Ωm, while resistivities in the vertical model range from 0.3 to 550 Ωm. The background resistivity varies with depth from approximately 0.5 to 4 Ωm, and the maximum value of the anisotropy ratio of the vertical to the horizontal resistivity is approximately 3. The water is isotropic with resistivity 0.3 Ωm, and the water depth varies in the range 1–2 km.

Figure 1. Acquisition geometry superimposed on a plan view of the SEAM model. Yellow dots are receiver locations. Green lines are source towlines occupied for the receiver located at east = 11,000 m, north = 26,750 m. Source locations are spaced every 100 m along each towline; the distance between the towlines is 1 km. Synthetic CSEM data were simulated for a total of 494 receivers with 22 towlines for each receiver.
1012 The Leading Edge September 2012

Figure 2. (top) Cumulative reservoir thickness (left) and 2.5 km vertical resistivity in Ωm (right) maps for the portion of the model over which CSEM data were simulated. (subsequent rows) Maps of the log of the EW component of horizontal electric field magnitude (Ex in V/m) at 5 km source-receiver offset. Note that log values are all negative so regions with higher field are shown in red. Source due west of receiver. Field is plotted at midpoint between source and receiver. Results for various frequencies are shown. Note the area of a relatively higher field to the east of the salt body, indicative of resistive hydrocarbon-filled reservoirs.
September 2012 The Leading Edge 1013

The SEAM model was initially constructed as a tilted transverse isotropic (TTI) model with bed-parallel and bedperpendicular values of resistivity. Because it was uncertain as to whether it would currently be possible to simulate using this type of TTI anisotropic resistivity model, it was decided to use a simplified VTI-type model with the geographically directed vertical and horizontal resistivities that were derived from the bed-normal and bed-parallel models. Details about the use of the petrophysics in the development of the SEAM resistivity model and how the vertical and horizontal resistivity values were determined from the bed-normal and bedparallel values are given in the SEAM update in The Leading Edge in February 2010. CSEM acquisition geometry and data CSEM data were simulated for a total of 494 receivers located on the irregular seafloor of the SEAM model. For each receiver, sources were located along 22 tow lines having a spacing of 1000 m. Towlines were located along 11 EW lines and 11 NS lines for each receiver. Each towline had a length of 20 km, extending 10 km away from the receiver or to the edge of the model if the receiver is located less than 10 km from the model boundary. Sources were spaced at 100 m along each towline for a total of 201 source positions along each towline. The source is a 1000-m long horizontal electrical dipole (HED), oriented parallel to the towline. Figure 1 shows a schematic of the receiver positions and towlines superimposed on a map view of the SEAM model. The model responses (the six electromagnetic field components Ex, Ey, Ez, Hx, Hy, and Hz) for 11 source frequencies of 0.025, 0.05, 0.1, 0.2, 0.4, 0.8, 1.2, 1.6, 2.0, 3.0, and 4.0 Hz were simulated. Figure 2 shows an example of a small portion of the CSEM data that have resulted from the simulations on the SEAM model. The figure shows plan-view plots of the EW horizontal component of the electric field magnitude (Ex) at various frequencies for EW source-receiver offset of 5 km. The field measured for each source-receiver pair is plotted at the midpoint between the source and receiver. Included for reference are maps of cumulative reservoir thickness (top left) and the vertical resistivity (Rv) model at 2.5 km depth (top right). Cumulative reservoir thickness is the sum of the thickness of all reservoirs below a given location. Reservoirs are above and below the salt in addition to locations away from the salt. The red region in the resistivity model shows the horizontal extent of the salt at 2.5 km depth. Visual analysis of the CSEM data shows that a high electric field occurs roughly above the outline of the salt body at this depth. Other than the strong field over the salt, one can see a zone of relatively high field east of the salt. This zone of high electric field is most pronounced on the higher-frequency data. This high field region is over a section of the model having several overlapping hydrocarbon-filled reservoirs at depths less than 5 km. Data from the CSEM simulations may be evaluated separately or combined with analysis of seismic, gravity, and MT simulations via modeling- and inversion-based methods. While EMGS conducted rigorous quality control (QC) on its simulation results, SEAM also conducted its own QC on the resulting simulations with the help of Irina Filina from
1014 The Leading Edge September 2012

Hess and Shangli Ou from ExxonMobil. Both Hess and ExxonMobil are SEAM Phase I RPSEA participating companies. We are grateful to Krishna Kumar, Daniel Shantsev, Friedrich Roth, and Trude Støren of EMGS for their excellent work in conducting the CSEM simulations. Other activity in SEAM Phase I RPSEA SEAM continues to conduct simulations on models derived from the SEAM Phase I acoustic model. Recently, the simulations on an acoustic TTI model have been completed by Advanced Geophysical Technology (AGT). A total of 35,747 shots were simulated using a source that has a bandwidth extending from approximately 1 Hz to 30 Hz. The resulting data are currently undergoing QC at WesternGeco. Some data that have passed through QC have been delivered to SEAM participating companies. Simulation of shots has now begun on an elastic version of the model. A total of 16,715 elastic simulations will be conducted for SEAM by AGT. As of the end of July, approximately 2000 of those shots have been simulated. Data being acquired include an array of receivers located near the surface of the water, four-component VSP receivers in five wells, and four-component seafloor receivers. Simulation is expected to be complete in early 2013. Elastic data will also undergo QC with a target completion date of early in the second quarter of 2013. A Request for Bids for the MT simulation is nearly complete and will be distributed to interested simulation contractors in August. — Michael Frenkel, SingularEM and Michael Fehler, SEAM

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THE METER READER

Coordinated by Robert Pawlowski

Pioneering water-bottom gravity: Jack Weyand’s amazing life and career—Part 2
Pat Millegan and John Bain

n 8 December 2011, John Bain, Guy Flanagan, and Pat Millegan sat down with Jack Weyand in ConocoPhillips’ conference center. Their goal was to capture the great stories Jack has from his 60+ years in exploration and as many years with SEG. In Part 2, they document pieces of geophysical history of which some of us are aware, but what a rare opportunity it was to hear this from the guy who was there on the ground participating! Sidney Schafer & Associates’ magnum opus is still for sale, the water-bottom gravity data set for the Gulf of Mexico (GOM) out to the shelf edge, or roughly 600 ft water depth, and even deeper in Mississippi Canyon, where stations were acquired on the seafloor in about 2500 ft of water. Acquisition started in 1955 (Figure 3). These data are licensed and used by all major oil companies and are still available through Fugro Gravity & Magnetics Services. Jack was involved with the planning, execution, and marketing of these data from day one. Part of that marketing included the knowledge of lease holders in the GOM as well as knowing new lease holders following each bid round. Thus begins an interesting story about an incredibly arduous and historic exploration program. JOHN: Jack, are you allowed to say who the first funders were for these surveys?
JACK: Yes, primarily it was Exxon, Texaco, CalCo [California Company, now Chevron] ... and Superior. We originally had a three-company limit for a year and then we expanded to four ... that would basically cover the cost of the survey. For most of the surveys, we had funding prior to starting the survey. Those primary initial participants were strong-minded gravity companies. They used gravity data.

O

JACK: A jeweler. He gave them as gifts to his clients. (Laughter) Those maps were an interesting project. I’d go over to New Orleans to the lease sales and jot down all the bids. I’d come back to Houston that same night and give them to my draftsmen. They had to make overlays for the four colors. Quite a project! Anyway, they were ready when I walked in from New Orleans, and within less than a week, we’d have a map printed and sent out. We’d get calls from everybody wanting that map. One particular mistake I remember ... we had Sun Oil Company with a lease that they didn’t own, but we had it on our map. I told their manager about it and he said, “Well, we’ve bought leases for less reason than that.” (Laughter)

With modern-day, high-quality GPS positioning it is impossible for most of us to comprehend the positioning and logistical challenges facing data acquisition well offshore in the GOM in the 1950s. Technical innovations and a well-thought-out procedure for ensuring precise geographic positioning made SS&A a resource as well as a premier contractor.
JACK: The surveying method was easier with seismic. Float the cable or drag the cable and you cover ground. You didn’t have the obstacles. You’d have straight lines. Gravity wasn’t used because it was more expensive!

(Pat and John chuckle ... wow, there’s a flip-flop!)
Earlier surveys started in shallow water with the meter on a tripod. Surveying on land was inexpensive but surveying offshore had problems with line-of-sight. That limited how far out you could survey. Then along came electronic positioning, with their hyperbolic systems for making measurements. But those systems had a pitfall with the sky waves which would sometimes interrupt the electromagnetic waves that were sent. They had two sets of [transmission] towers. We were relying on a red and a green reading, which would give you a position, out as far as you wanted to go. The most predominant technologies for positioning out there at the time were RAYDIST and LORAC. They used similar principles, but they had their towers in different spots. Therefore, there were different sets of transmission bands and their paths were different. You had to know which system it was if you were going to go back and rework an area. If you came back later and you had a reading for a position with the “C” network, you would have to know what it would be if you came back with a “B” network, because it would plot in different spots. We shifted the stations to the best geographic location, some of them as much as 3000 feet.

PAT: Believers! (Laughter) Once upon a time, maps were hardcopy, printed-paper maps, unlike the bits and bytes on a screen today. SS&A put out a valuable index map of the GOM that showed blocks and lease holders.
JACK: You know those index maps we put out? They were real popular and a real good advertisement for us. We got good mileage out of those maps. At the time we first produced them, we didn’t give them to the drilling and production people ... we sold them for $5 bucks. We finally started giving them to everybody. One time we had an order for a dozen of them from a jeweler in Lafayette.

JOHN: A jeweler?
Editor’s note: The is the second half of a two-part article. The first half appeared in the August issue of TLE.
1016 The Leading Edge September 2012

JOHN: RAYDIST and LORAC ... I remember you telling me a story. Did they come to you because you had better corrections than they did?
JACK: That’s what Ed McClure at LORAC always said,

THE METER READER
JACK: Yes. The cable got sheared. I wouldn’t be surprised if someone has found it, because they do a lot of diving on Flower Garden Banks. It gets up to around 50 feet of water depth. They’d spend a couple of days looking for them. The one over east of the Mississippi ... that mud ... you don’t know how much there is in some places!

Our friends at Micro-g LaCoste tell us that a 1960s cost for an underwater gravity meter was US $30,000. According to the Web that is about $200,000 in today’s dollars. So the loss of two of those meters in the 1950s was significant! Micro-g LaCoste said a 1950s cost may have been $25,000, but they do not have any documentation. The last underwater meter sold by LaCoste & Romberg was around 2002, and it sold for $150,000.
Figure 3. The Sidney Schafer & Associates historic water-bottom gravity survey for the Gulf of Mexico out to 600 ft water depth and up to 2500 ft in Mississippi Canyon. “How accurate is our survey in this area?” We had created a LORAC position accuracy map. It had all the places where we had taken dual-receiver readings, and all the places where we had taken readings on rigs with known locations. You could tell the accuracy of the network. Somehow I lost that map. We were cleaning out files, and I’m afraid my LORAC positioning accuracy map was lost. Somebody probably has that map. We gave copies of it to clients. It would illustrate all these things. JACK: What turned the corner with doing the surveys was when Sid started group programs. The cost factor with group surveys made it happen. Some of the companies, CalCo, Exxon, Gulf, Phillips, had run surveys on their own. I’ve never seen those surveys. But being quite frank about it, I don’t think they were as stringent about the positioning. The positioning wasn’t as good as our surveys. And I don’t know what meters they used. I know GAI/GMX ran some surveys. [The real innovation] was the remotely controlled underwater gravity meter that enabled us to meter in water depths up to about 120 ft. It was the major factor in expanding the gravity [in the GOM]. Later we could meter in deeper water depths. At the time there were two meters that were common; one was the LaCoste Underwater Meter and the other was the North American Meter. Those two meters came from Lucien LaCoste [LaCoste & Romberg] and Reg Sweet at North American. They were both involved in the development of the zero-length spring. You could measure precisely. They had a damper, and it was a remotely controlled, integrated system. The meter was lowered to the seafloor with a cable. You’d level the meter remotely with a gyroscopically controlled level. Then you’d release the damper. You could be in pretty rough seas and still get a good meter reading. You’d watch the dial. On a calm day you’d have a null when the meter was stable. On a rough day, when the seas were high, you could still meter, but they’d have to choose an average of how the dial was swinging. You could get a good reading, but you had to wait long enough to get a good average. Sometimes metering down on the [Mississippi River] delta you could get a new reading every time. The delta is apparently always shifting. It was very difficult to get a reading. At the same spot it was possible to get readings [off by as much as] 10 gravity units (1.0 mGal) if you didn’t wait long enough. You’d think you had a null. Come back the next day and you’d get a big variance. That was a problem even with that remote controlled system. If it were too rough, you’d get bad data. So we’d choose a sea state the best we could. Initially we measured the water depth with air pressure relative to sea-bottom pressure.

JOHN and PAT: It would be good to get a copy for the SEG museum.
JACK: It was extra effort to get the accuracy of the true geographic position. Converting electronic positions to true geographic positions involved various systematic errors. A lot of times weather affected it, including geomagnetic storms, which would disrupt the signal. You run a survey line and you know where you are on this one. Then you’re out in the middle of nowhere and the signal goes away. According to the boat speed you’d have to try to tell how many lanes you missed. And hopefully you’d tie at the end to a known position. But you didn’t always succeed at that. If you lost your position, you would have to come back to the base to reestablish where you were. Those were just problems that we ran into. We had bases about 5 miles apart in a grid, and we kept close ties (Figure 4). You would run a survey with a set of specifications. [Such as] what kind of tie you expected to have, and what drift you would allow. We usually used a maximum of three hours drift. We would have a base loop, which would be 5 miles by 10 miles, and we’d have to tie that loop with the bases within 0.2 mGal. It is essential to have as close [positioning] to the base as you can get in order to repeat.

JOHN: You told me once about a gravity meter that never came back up.
JACK: I know of at least two meters that have been lost. One got buried in the mud somewhere over east of the Mississippi River and one out on Flower Garden Banks, off the Texas and Louisiana border.

PAT: Was that in the diving bell itself?
JACK: No, it was just a hose, a separate hose. You’d have one armored cable that would have I think about 12–13 conductors (Figure 5). It would include the meter reading and the gyroscope controls. There was another cable that was the
September 2012 The Leading Edge 1017

PAT: So it got hung up on Flower Garden Bank?

THE METER READER
the station, we’d measure the known locations of rigs using the stadia method used by the Navy. If we knew the height of an object at a distance, we’d bring them stereoscopically into focus. We’d get on all four sides of a rig and establish a position. Then we’d take the intercepts of that position so we had a trapezoid and that would be the best location without distortion from the rig. An example of when that was real important ... there were three [transmission] networks; one network for Louisiana, one for Texas, and one for east of Louisiana and Mississippi. At the time a lot of people didn’t know this, but when we went from one network to the next, the transmission media weren’t uniform. So if we had one set of towers to the east and one set of towers to the west and we had a network for each, and if we put a meter [location] from say the B-net and a location from the A-net and we established a position, we’d find that they were in different places. Our position could differ by as much as 3000 feet. So we plotted and compared the two. We knew we were at the same place, but they didn’t plot the same. It was because the systems weren’t uniform. In some places our transmission was across salt water, in some places over land, and in some places over fresh water. So this was a necessary thing, knowing that we had the best geographic position. [Once that was determined] we would use that as a piece of the known data. It came in handy that we did that. After a hurricanes came through West Cameron and destroyed the network, they had to reposition transmission towers, and we couldn’t tie anything. Nothing would tie! [This was] a continuation of a survey, so we went back and surveyed with electronic positioning of a couple of known rigs, and we saw that they were about 500–600 ft different in location ... so when we corrected by that amount it all tied.

Figure 4. A base loop map provided by Jack Weyand. Schafer acquired the data similar to a land survey with rigorous base ties. In red, Jack shows a sample of the actual station spacing in the GOM water-bottom surveys.

air cable to measure the water depth. You’d record what the pressure was at the bottom. Then from that you’d derive the water depth. It was very difficult to work those two cables. The meter was lowered either over the side or the stern of the boat. We had capable skippers that would maneuver the boat, while we had the cables down. He had to keep the cables out of the propeller. That was always a big challenge, but deeper than 100 feet or so it started to be a big problem. Eventually, piezoelectric-type pressure gauges came along that would measure the pressure with a gauge that could be attached to the meter and transmitted up through the cable. Then we just had one cable.

Water depth and tide corrections posed a significant challenge during the acquisition of the water-bottom gravity data. SS&A used creativity and common sense to tackle these technical issues in a rigorous fashion. JOHN: You mentioned 120 ft as maximum water depth, but when did you get beyond that limitation?
JACK: It came about by necessity. At first people didn’t want to go beyond 120 ft of water. That was as far out as the Bureau of Land Management had subdivided. When they subdivided again, we’d run a survey over it. You have an area like Eugene Island and then you have Eugene Island South Addition. The South Addition went from 120 ft of water out to 600 ft. We were at the mercy of the water depth and handling that cable at those depths. We went from 600 ft of water in Mississippi Canyon down rapidly to about 2500 ft. The improved meter would go as deep as the cable would allow you to go. But in a couple thousand feet of water, it’s a long cable to have out there. We had a problem that the meter could be maybe 300 or 400 ft away from where the mast of the surveying system was. We compensated for it with a diagram showing the meter was out so-many-feet, offset say to the southeast of where the boat location was. The boat location was what the positioning survey told you. Our deepest data in Mississippi Canyon goes out to 2500 ft water depth. There’s a problem I’ve been working on for de-

JOHN: And when were these innovations introduced?
JACK: It was about 1955 when we first started using the remote-controlled gravity meter (Figures 6 and 7). And that was about the same time electronic positioning, LORAC and RAYDIST, were coming aboard. You could measure out as far as you wanted, but accuracies were reduced with increased distance. In our projects, we kept close track of what the real geographic position was as well as the relative position. Those were the two factors. The relative position was always about as good as you could ask for. We discovered that the geographic locations were different if they were transmitted over salt water or fresh water. To keep track of the true geographic position of
1018 The Leading Edge September 2012

THE METER READER
JACK: Yes, that would be when we didn’t get the null value right. Some of the stations we’d misjudged the null because of unstable ground or sea state. Like out in the [Mississippi] delta ... that big old fan floating up and down ... noise ... that’s what we were measuring. Sit there long enough and you get a null. If you don’t, you get weird readings. We’d always put a letter by a station. The first time it was occupied it was the station number. The next time it was A, B, or C or whatever. I could show you one on the map right next to the Mississippi River that went up to H or I ... something like that.

PAT: It’s interesting that the delta is that dynamic.
JACK: Well, it’s changing all the time. I’d like to compare our bathymetry maps against the current ones. I bet there’s quite a bit of difference.

Figure 5. A piece of cable used offshore Gulf of Mexico for gravity surveying (provided by Jack Weyand).

cades! That canyon has a fill of more recent sediments. There’s a couple thousand feet of low-density mud in that trough that causes a huge minimum gravity anomaly. That’s the background effect that you’d like to get out of there to give you a better regional for your gravity. We were always advocates of integrated interpretation of geological and geophysical data. Large amounts of geophysical and core data ... used in geohazard surveys ... exists in engineering departments. It’s data, which should also be used by exploration departments. I think density is a problem out there that eats our lunch! We can take a [modeling] program and calculate what this [salt mass] is. Well, the density variation in particular with that monster low-density mud like in Mississippi Canyon (Figure 8) eats our lunch right quick. In earlier days we thought we were so smart, telling what the salt looked like. My first experience with that was when we got some layered salt in a well ... and we found no anomaly! It wasn’t massive salt, just stringers. So I think density is still a challenge.

Of course in a dynamic environment like the Mississippi Delta, there is no doubt that the bathymetry is different today. It would be interesting to compare the SS&A maps with modern data to see how much it has changed. This can have some bearing on mathematical data reductions and modeling. The proper height of the water column above the meter should be the vintage water depth. If the amount of change is small, then there is no problem.
JACK: We tried to make tidal corrections. [Tides] affect your calculated values. We didn’t have a good measure of [the tide] offshore. The gauges were all at the docks. The tide there wasn’t what the tide was offshore. We tried to judge the tides by repeats of water depths. If we got a different water depth, part of that was tide. We tried to establish what the tide was by what the differential was every time we came back to the base. It was kind of a laborious procedure, but it was a way of trying to get everything leveled. Ideally we could establish a tide chart by always going to a slowly changing or flat bottom. We measured it there and if we came back to that same approximate location and it was a different water depth, then it was the tide. That was our best attempt. So you can see we had all kinds of things trying to slap us in the face.

The term “salt nil zone,” often used in gravity modeling around salt is attributed to Jack Weyand. Nettleton and others used the term “cross-over depth” to identify the single depth at which salt and surrounding sediments have the same density, and therefore no gravity anomaly is created. Jack coined the term “salt nil zone” to help us recognize that this low-density contrast exists over a thickness of our rock column, rather than thinking of it is as one solitary depth. JOHN: I remember when Edcon started to digitize the waterbottom gravity for you. You said that you really scrutinized any suspicious points during acquisition and if any were found they were remeasured, often at great cost to ensure the data were fully scrubbed. Even though it appears to have single-point bloopers, you made sure they were verified anomalies. I also remember being amazed at some of the stations, where you’d have five or six repeats!

JOHN: I’m trying to imagine what it would have been like to start those surveys. After your experiences, somebody could follow your example. There wasn’t a lot of that work that had been done before, right?
JACK: No, we were pretty much figuring out each piece as we went along.

JOHN: It seems like a daunting task.
JACK: When you look at a geophysical survey, you have all these measurements that you have to take to achieve an accurate measurement of the Earth. You have all these corrections you have to make, which can affect the accuracy of the data. [If] you could run everything instantly on the same day you’d have a lot of problems taken care of. We had to set specifications. Originally California [Company] did it. And I agreed with them, the specs were very well thought-out.
September 2012 The Leading Edge 1019

THE METER READER

Figure 6. An underwater gravity meter system (courtesy SEG museum archives).

Figure 7. The cable drum and winch system with the underwater meter. This image is actually two photos on one vintage glass slide (provided by Jack Weyand) that makes it look like one system and one photo. JACK: Yes, and that does not [make it their data]. You’ve got to have the basic data itself! It cost a lot of money to run those stations. And somebody takes that data and sells it for as little as 10% of what it would cost to actually acquire it. It’s pretty obvious to me, but some people have strong feelings about this. Once they change the data, they call it an interpretation. But the value of it is the basic data [marine operations, positioning and metering]. Compared to the cost of an interpretation, it’s a wide margin. I put up with [knowing our data were compromised] for 10 years before evidence came to light.

SS&A’s ambitious GOM gravity data set was first sponsored by and available to the funding companies as Jack mentions above. After a proprietary period, SS&A was allowed to license the data to other companies. Over time, they became aware that their data might have been bootlegged. Over a ten-year period Jack became a passionate, precedent-setting advocate for the protection of data ownership rights.
JACK: There was an occasion where I got a guy in trouble. He called to talk about what data they had. I go there, and he puts the map up on the wall and there’s one area that I could recognize. I could tell where it was because of the station patterns. I said, “this data belongs to us,” and that started it! It was a pure violation by a contractor who got our data, falsified it, and put the stations in a [slightly] different spot. The stations were identifiable because they made them into loops just like our loops! And the differential between those maps and our maps was something like 0.7 mGal. The values were all different by that amount ... purely identifiable!

JOHN: Really? It dragged on that long?
JACK: Knowing that something was the matter ... people would tell me, “I think so-and-so has your data.” But I didn’t know for certain. Then I walked into one company and I saw the data on the wall. And they denied that there was any wrongdoing, and we had to go to court. We had two or three other cases where I made the discovery, and they just went ahead and paid for the data because they wanted it. Then it was cleared up. There were other cases too. [This one company] was adamant that they were going to beat us. I got a call from the exploration manager. He said that they had a hot-headed young lawyer that was going to be on this case. He tried to scare me off. But by that time I was “in the ditch.” Those lawyers are tough and can be insulting. That was no fun!

JOHN: So there was a contractor who was selling your data?
JACK: Yes. The oil company couldn’t be blamed, but they should have been more diligent. [It went to court] and I had to spend two days on the witness stand. Those attorneys didn’t quiz me on the stand. Boy, they took depositions like crazy though! We had a clear case. I had 30 exhibits, and I had to educate the jury on geophysics, and on what the “game” was. I had already been talking a whole day on the subject and I remember this one juror ... she was just shaking her head. They never cross-examined me, and, in the end, our data rights were protected. Some people have a different attitude about geophysics, particularly about the ownership of data. Data has a value, and it has a value as long as it is still owned and marketed. If it gets out in the public domain, you’re out of business. Some companies think when they have altered the data in some way that it’s their data.

JOHN: I know that other people who own data look back on that and feel that you created a precedent for them to stand on. Were there other similar cases around at that time? I mean other companies ... seismic brokers for example?
JACK: Seismic brokers had the same problem. I’ve been in people’s offices to review a farm-out and I’d have questions in my mind about seismic ownership, but I couldn’t get in there and police their data for them; but we did police our data.

PAT: Like recalculating with a different Bouguer density?
1020 The Leading Edge September 2012

Jack Weyand has been an active and enthusiastic member of SEG for 61 years. In 1995 he was awarded Life Membership.

THE METER READER
box, so they could keep track. There were many times on the bus going to the convention center a guy would say, “Hey Jack, drop these in the box for me.” (Laughter) Another year the annual convention was in Mexico City. And that was almost a disaster because companies couldn’t get their equipment [into the country]. A lot of booths didn’t get manned, and they didn’t have enough power. David Yowell got in touch with people in Hollywood and they shipped generators down to Mexico City so they could run the thing. There were a lot of booths with just a sign ... “So-and-so couldn’t get their equipment in.”

Figure 8. Geologic cross-section sketch by Jack Weyand combining core data, seismic, and gravity, indicating the trough of low-density mud is causing large, long-wavelength gravity anomalies. The profile illustrates the use of seismic data (Coleman and prior SPE Publication #33) and soil boring data (from “Shear Strength Atlas” for the Gulf of Mexico by McClelland, 1979) and Schafer Underwater Gravity.

Sidney Schafer & Associates has published the Geophysical Directory since 1946—a few years before Jack started with SS&A. Jack spent many painstaking hours patiently walking the SEG convention floors to make sure that all new companies were included, and to remove any that were no longer around. At one point in our chat, Jack mentioned that geophysics had been good to him.
JACK: Oh, I’ve run into some tough projects. One of them I took on just before Christmas one year. I worked on that thing all during the Christmas holidays. And it just didn’t make any sense. I could not get a solution! This was a seismic prospect. When I handed it in, I said to the client, “This is the best I can do. I don’t even think you have to pay me because I don’t think I have a solution.” Nothing fit!

Jack also served the Geophysical Society of Houston (GSH) as secretary (1965), as vice president (1969), as president (1972), as student loan chairman, and as SEG Council representative. The GSH awarded him life membership in 1980. He also served during SEG Annual Meetings as Publication Chairman (1966), Technical Materials Chairman (1971), and Vice General Chairman (1980, 1986). Jack has also been an active member of the SEG Gravity & Magnetics Committee. PAT: How has SEG changed over the years?
JACK: I guess in how big the administration has gotten. SEG conventions used to be [planned differently]. Howard Breck was the SEG business manager, and he handled the exhibits. All the other functions were handled locally. I had the Publication Committee one year. It was amazing how much was involved in printing all the things you needed for a convention. Everything you’d look at had a schedule. At that time we had to collect biographies and pictures from the speakers, and that was all in the book. I still have some of the first books. [Publications] was a big committee, and it touched everything. Now all that work is done in Tulsa ... all the printing and a lot of the scheduling.

JOHN: Did the client ever give you any feedback?
JACK: No. I think it wasn’t too far from what they wanted to hear. But I never heard otherwise. And I did some work for them afterward, but (Jack chuckles) it just ruined my whole holiday season! I had cross sections all over the place! But the field has been good for me. I feel I’ve never worked a day in my life.

(John and Pat laugh.) PAT: I wish I could say that! Jack Weyand is a rarity in that he was an entrepreneur on the front lines of technical innovation, data acquisition, and data brokering through an interesting time. His infectious good cheer, his optimism, and his enthusiasm for geophysics in general help to document timeless stories from an important period in our exploration history. With a smile we say, no matter how you feel about your career, life stories from our industry are at least interesting. Hopefully your career has more ups than downs, has opportunities, successes, and a minimum of nonuniqueness and dry holes. Take the time to talk to your friends and then share their stories too. We will all gain from it.
Acknowledgments: The authors thank Guy Flanagan and ConocoPhillips for generously hosting our interview in their world-class conference center. We are also grateful to Tom LeFehr, Alan Herring, Lee Lawyer, and Jerry Henry for their contributions and insights. Corresponding author: [email protected]
September 2012 The Leading Edge 1021

PAT: Did the conventions always move around?
JACK: They always moved around but had a broader range than they do now. They used to regularly have them in Houston, Denver, New Orleans, Los Angeles, San Francisco, and Calgary. One year they had it in Atlanta, and in Washington, D.C. [Now] they have a close net of [cities]. I understand they are going back to Las Vegas. One year, Las Vegas was a disaster because everybody went to the casinos, including myself. (Laughter) I think it was in Calgary, where they did this scheme to keep track if people were going to the convention, and not to the city. Every session you attended you had to drop a card in the

The Leading Edge

Signals
Dear Editors: I’d like to take this opportunity to thank SEG for the response I got to a request for help in researching information for a high school project this past school year. My request was prompted when my granddaughter, Reilly, asked me if I knew anything about “frequencies.” Her sophomore math class had been assigned to develop a poster and a verbal presentation to explain frequencies, and her math teacher was awarding a US $20 prize out of her own pocket for the best presentation. Because I processed a lot of vibroseis data 40 years ago with Texaco, I claimed to know quite a bit. I then hedged my bet with an e-mail to the SEG office, asking if there were PowerPoint presentations or videos available to incorporate into such a presentation. I addressed my request to “Online Membership” (I’m not sure why), and my request soon went through Ricky Shafer, Tom Agnew, Bob Wyckoff, and Lisa Buchner. Within a week or so I had a PowerPoint attachment, a CD, and links to several other sources. Reilly put together an excellent poster presentation and gave an excellent talk; however, she placed second—her choice of a consultant was likely her downfall. She also cannot appreciate that someone with a Conoco background helped in all this. I’ve been trying to nudge her toward the Colorado School of Mines, but she wants to be a doctor—even though a burro with a stick of dynamite in its mouth is a much, much cooler mascot than an ugly, hairy buffalo. This exercise reminded me of the almost-magical nature of the vibroseis technique. To think someone thought we could even introduce a sweep of frequencies into the ground where they’re compressed, sheared, converted, inverted, refracted, and then reflected back to the surface to be recorded 20 seconds later is almost unimaginable. Then again, to put together a processed volume of data that provides a visualization of more than 1000 cubic miles of the subsurface is, also, almost a miracle—like the sonogram of a fetus, only bigger. I’m certain Lee Lawyer will quibble over the concept of a frequency being sheared. In a recent column, he discussed tape formats, and this technology can also be puzzling. How did we record 24 traces on 21-track one-inch tape, and then advance to recording 48 traces on 7-track half-inch tape, and finally to recording 96 traces on 9-track half-inch tape? I wouldn’t be surprised if some companies are now recording more than 100 traces on quarter-inch tape. Thanks again to SEG and your (our) commitment to the support, response, and advancement of youth education. —Jim Wood Delta, Colorado, USA

Multicomponent Seismic Technology
Bob A. Hardage, Michael V. DeAngelo, Paul E. Murray, and Diana Sava Much has changed since SEG published a comprehensive book on multicomponent seismic technology in 1991. The current volume, Multicomponent Seismic Technology (SEG Geophysical References Series No. 18), brings the subject up to the present. Emphasis is placed on practical applications of multicomponent seismic technology, with chapters dedicated to data-acquisition procedures, data-processing strategies, techniques for depth-registering P and S data, rock-physics principles, joint interpretations of P and S data, and numerous case histories that demonstrate the value of multicomponent data for evaluating onshore and offshore prospects. All forms of multicomponent seismic data are considered –— three component, four component, and nine component. Interpretation focuses on elastic wavefield seismic stratigraphy, in which a seismic interpreter gives the same weight to S-wave data as to P-wave data when defining seismic sequences and seismic facies. S-wave splitting in fractured media and other key theoretical concepts are supported by numerous data examples. The book will be of interest to researchers in multicomponent seismic technology and to explorationists who have to locate and exploit energy resources. The book will be appreciated by those who shun mathematical theory because it explains principles and concepts with real data rather than with mathematical equations. ISBN 978-56080-282-2 Catalog #178A Published 2011, 336 pages, Hardcover SEG Members $79, List $99, e-book $99

E-mail: [email protected], Order publications online at: www.seg.org/publications
Hardage-Multicomponent Half Ad.indd 1022 The Leading Edge 1

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

An introduction to this special section: Geophysics in reserves estimation
Robert Lorenzen, Maersk Oil Henk Jaap Kloosterman, Shell International B.V. William Abriel, Chevron

stimating oil and gas reserves is one of the most important functions for petroleum companies to support portfolio management and revenue forecasting. The investment community uses reported reserves to assign values to companies or to individual projects, which is important to the stock markets and for financing projects. Governments use reported reserves for regulatory oversight and for forecasting national petroleum production. In 2008, one of those government agencies, the U.S. Securities and Exchange Commission (SEC), published “Modernization of Oil & Gas Reporting.” The document was based on numerous recommendations to update reporting rules for oil and gas companies to reflect the advance in technologies. The new rules took effect in 2009, and set the stage for a stronger integration of geophysical technologies in petroleum resource estimation and reserves reporting. In 2008, SEG set

E

These papers show a wide range of applications of seismic data for resource and reserves estimation. A common theme is correlation of seismic to well results and production data.
up the Oil & Gas Reserves Committee to facilitate the involvement of the geophysical community in the field of reserves estimation. It was recognized that geophysics has an important and perhaps expanding role in reserves calculations. The contribution of geophysics can be divided into static and dynamic reservoir characterization. For static modeling, any geophysical methods that can delineate the reservoir, estimate the lithology, porosity, or fluid content; or estimate the relative depth of the reservoir to the fluid contacts are of particular importance. Seismic AVO and flat-spot methods, acoustic and elastic inversion, and depth conversion are but some of the clear examples of geophysical technologies used in static modeling. Emerging new technologies like CSEM also have the potential for fluid delineation. For dynamic modeling, 4D seismic is now a proven technology, and can help not only in reservoir management but also directly in reserves estimation. Experience tells us that all these methods sometimes work really well and sometimes not, depending on geological setting and production history. Integration of geophysical and geological analyses results with available production data is necessary to estimate reserves. It is therefore important and up to the individual companies to justify that given technologies are reliable in the context they are being used on a case-by-case basis. This month’s special section is the first collection of papers to address the role of geophysics in petroleum resources and
1024 The Leading Edge September 2012

reserves estimation. The articles comprise a wide range of different perspectives and experiences, from review of guidelines for classification of resources, use of seismic data to establish fluid contacts, to examples and reliability of direct hydrocarbon indicators. The focus of the articles is on the impact of seismic technology on resource evaluation. The first paper, “Introduction to the Petroleum Resources Management System and the implications for the geophysical community,” by Lorenzen et al. is a review of the industry guidelines for resource classification and estimation also known as PRMS. Sponsored by many of the leading technical societies (SPE, AAPG, WPC, SPEE, as well as SEG), PRMS has become a global standard for resource evaluation. One of the fundamental criteria for a petroleum accumulation to be called reserves is that it can be commercially recovered by application of development projects under defined conditions. Projects are therefore essential to the classification of resources and broadly dependent on the project’s chance of commerciality. The three classes of resources are: Reserves, Contingent Resources, and Prospective Resources. While the classes reflects the maturity of the projects, the uncertainty in estimated sales quantities of petroleum is given by a best estimate together with a low and a high estimate. The paper also discusses a couple of hypothetical examples relating the value or use of seismic data in classification and categorization of resources. The second paper, “The role of geophysics in petroleum resources estimation and classification—new industry guidance and best practices,” by Kloosterman and Pichon, zooms in more specifically on the guidance provided in the PRMS Application Guidelines issued in 2011, supplemented with some case study examples from several IOCs illustrating the impact of applying geophysical technologies for assessing structural definition, reservoir development, fluid contacts and movements and for flow surveillance. An integrated interpretation with production history, static and dynamic data is the most effective way of assessing reservoir extent and performance. In “Resource assessment based on 4D seismic and inversion at Ringhorne Field, Norwegian North Sea,” Johnston and Laugier discuss how Vp/Vs from elastic impedance and 4D seismic together helped extend the mapped oil-bearing part of the reservoir based on the observation that 4D seismic showed water sweep in an area previously thought to lie below the original oil-water contact. This area had no well control, so the 4D data in addition to the fluid response from the water sweep help constrain the time-to-depth conversion. The Vp/Vs interpretation helped extend the mapping of thin sands updip. The implied increase in volumetrics was supported by production data suggesting larger in-place volumes than previously estimated.

Geophysics in reserves estim ation

In “Seismic technology supporting reserves determinations: Gorgon Field, Australia,” van der Weiden et al. describe a strategy for using seismic and inversion data to estimate reservoir continuity. Gas condensates are located in fluvial channel sands that are acoustically soft. The strategy includes defining with reasonable certainty the reservoir tank, then establishing the internal continuity of the reservoirs, and finally the reservoir properties and their continuity. The analysis of seismic and well data creates a case static model, which is then combined with uncertainty analysis in a probabilistic way. The emphasis is on reasonable certainty to demonstrate clearly the reliability of the data. In this way, the seismic interpretation and quantitative interpretation in combination with integrated reservoir modeling and engineering data were used for reserves booking purposes under the principle-based SEC rules. Pichon et al. discuss the use and confidence assessment of direct hydrocarbon indicators (DHI) for resources evaluation. In their paper “DHI support for resources evaluation: Confidence assessment examples,” they provide three examples to illustrate the integration of seismic DHI with well results. The examples are in contact and compartmentalization evaluations. Confidence assessment methodology necessary to evaluate the DHI robustness and certainty are emphasized. The methodology includes assessment of the seismic and petrophysical data quality, and their ability to properly represent the seismic response for the known (or expected) reservoir and fluid characteristics. Then the geophysical information is evaluated for consistency with the geological and dynamic knowledge of the field including their uncertainties. It is the cross-view between seismic technology and field production behavior that is key to confidence for reserves and resources evaluation. DHI reliability and statistical significance is also discussed in the next paper: “Relating seismic interpretation to reserve/ resource calculations: Insights from a DHI consortium” by Roden et al. Based on well results from 217 prospects around the world, analysis was made between AVO classes, DHI characteristics, DHI grades and success or failures of the wells. Most of the wells were exploration wells. The analysis of the full database allows for computation of a DHI index given a specific DHI prospect interpretation. A high index number implies a high reliability of the particular DHI in question; if the index number is low, then so is the reliability. The paper outlines a strategy for using the DHI index for resource calculations in exploration with regard to area determination or thickness determination. For instance, for area determination a weighted sum of the areas defined by geological observations and the area defined by the DHI extent can be used. In addition, for thickness determination, the potential of seismic tuning also need to be considered. Reservoir connectivity is important to reservoir engineering developments projects, as it directly impacts the planning of well patterns and well completion. In their paper “Stochastic volume estimation and connectivity analysis at the Mallik gas hydrate field, Northwest Territories, Canada,” Dubreuil-Boisclair et al. provide a probabilistic estimation of the spatial heterogeneity of gas hydrate grades and assess the connected natural gas volumes, at different grade cutoffs. Gas hydrate layers

have an ice-like structure of hydrates, which increases the stiffness of the sediment matrix. This causes the P-wave velocities to be significantly larger in highly saturated gas hydrate layers. Acoustic impedance from 3D seismic together with gas hydrate grade data from wells is used to obtain many gas hydrate grade realizations. A stochastic connectivity analysis is computed for each 3D gas hydrate grade scenarios. The connected natural gas volumes can be estimated together with its uncertainty, for each layer, at different cutoffs. Eastley et al. in “Case study: Using seismic inversion to constrain proved area definition” provide an example of the integration of seismic impedance data with production data as a basis for Proved Area determination. The depositional model is of a high net-to-gross large turbidite system composed of feeder channels that feed and incise into a background of lobes. Well test data clearly highlight that parts of the reservoir are producible at commercial rates. P-impedance data showed consistent low values for good well (production) areas, and high values for bad well (production) areas. The seismic inversion was thus deemed capable of discriminating between good and bad wells with reasonable certainty, and it was therefore used to extrapolate the lateral extent of the Proved Area into adjacent undrilled portions of the reservoir that can, with reasonable certainty, be judged to be continuous with it. The last paper is by von Lunen et al. and titled “Strategies in geophysics for estimation of unconventional resources” and is a discussion of the issues and techniques special to these plays. In unconventional resource plays, the deliverability system is a principal area that must be addressed by geophysics. This includes reservoir effectiveness, geomechanics, and stimulation treat. Reservoirs are often heterogeneous or fractured; identifying the fracture network and the stimulated rock volume is therefore an important component. Multicomponent 3D seismic surveys and 4D seismic surveys are established techniques for reservoir characterization. For gas shales with low permeabilities, the emerging technique of microseismic data is the geophysical tool of choice for estimating stimulated rock volume, future production, and recovery factor, because it is the only technique that can directly observe the creation of a drainage network within the stimulated resource container. These papers show a wide range of applications of seismic data for resource and reserves estimation. A common theme is correlation of seismic to well results and production data. Many different uses of statistical analysis, either qualitative or quantitative, serve to underline the importance of reliability of technology for the problems considered. While the issue of reliability of technology is central to rules governing reserves reporting, it is also important to any other applications of geophysical technology. Geophysics in reserves estimation is a new field. We hope that these papers can help our profession understand the associated issues and possibilities. At the SEG Annual Meeting in Las Vegas in November, a workshop organized by the SEG Oil & Gas Reserves Committee will offer an opportunity to share and discuss issues around these TLE specialsection papers.
Corresponding author: [email protected]
September 2012 The Leading Edge 1025

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

Introduction to the Petroleum Resources Management System and the implications for the geophysical community
Robert Lorenzen, Maersk Oil Satinder Purewal, EER (AS) John Etherington, PRA International

T

he Petroleum Resources Management System (PRMS) is designed to provide consistency in estimating natural occurring petroleum quantities, evaluating projects to commercially extract and market derived products, and present results within a comprehensive classification framework. PRMS is the latest result of international efforts to standardize the definitions of petroleum resources and how they are estimated that began in the 1930’s. PRMS was published in April 2007 and is jointly sponsored by the Society of Petroleum Engineers (SPE), the World Petroleum Council (WPC), the American Association of Petroleum Geologists (AAPG) and the Society of Petroleum Evaluation Engineers (SPEE); PRMS was subsequently endorsed by the Society of Exploration Geophysicists (SEG). In November of 2011 these same five organizations working through the SPE Oil and Gas Reserves Committee (OGRC) published “Guidelines for the Application of PRMS” (PRMS-AG) to provide guidance, additional details including examples. While PRMS and the supplemental guidelines provide an international technical standard for classification, external reporting remains subject to specific requirements of individual government and regulatory agencies. Since publication of PRMS in 2007, many of these agencies have chosen to explicitly reference PRMS or implicitly align with its underlying principles. For example, the U.S. Securities and Exchange Commission (SEC) used PRMS as a reference guide for its

publication in December 2008 titled "Modernization of Oil and Gas Reporting." The revised SEC disclosure rules essentially adopted PRMS definitions for reserves. Such disclosures provide investors with a sufficient understanding of a company’s oil and gas assets to support comparative valuations. PRMS defines reserves as those quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions. Reserves must further satisfy four criteria: They must be discovered, recoverable, commercial, and remaining (as of a given date) based on the development project(s) applied. SEC also introduced the definition of “reliable technology.” The definition permits the use of technology (including computational methods) that has been field-tested and has demonstrated consistency and repeatability in the formation being evaluated or in an analogous formation. This new standard will permit the use of a new technology or a combination of technologies once a company can establish and document the reliability of that technology or combination of technologies. Note, PRMS does not use the term “reliable technology,” but the intent is the same. Companies are not required to disclose proprietary technologies. The disclosure may be more general, and it can state that an integrated interpretation combining seismic data, geologic data, formation tests, and geophysical logs were used to calculate the reserves estimate. SEC staff, however, can as part of the review and comment process, request a company to provide supplemental data to support its conclusion that a technology or mix of technologies used to establish reserves meets the definition of reliable technology. Note that under SEC rules, companies are required to disclose “Proved” reserves, have the option to additionally disclose “Probable and Possible” reserves and are prohibited from disclosing resources not classed as reserves. Other jurisdictions, such as Canada, allow the option to fully disclose all resource classes and categories as defined in PRMS. SEG is now an active sponsor and contributor to PRMS and PRMS-AG. In the following sections, a condensed description of the definition, classification, and categorization of petroleum resources is given based on the PRMS-AG. Petroleum resources PRMS is a fully integrated system that provides the basis for classification and categorization of all petroleum reserves and resources. Although the system encompasses the entire resource base, it is focused primarily on estimated recoverable sales quantities. Because no petroleum quantities can be recovered and sold without the installation of (or access to)

Figure 1. Resources classification framework. On the vertical axis is project maturity; on the horizontal axis is technical uncertainty.
1028 The Leading Edge September 2012

Geophysics in reserves estim ation

the appropriate production, processing, and transportation facilities, PRMS is based on an explicit distinction between:
1) the development project that has been (or will be) imple-

mented to recover petroleum from one or more accumulations and, in particular, the chance of commerciality of that project; and 2) the range of uncertainty in the petroleum quantities that are forecast to be produced and sold in the future from that development project. This two-axis PRMS system is illustrated in Figure 1. Each project is classified according to its maturity or status (broadly corresponding to its chance of commerciality) using three main classes, with the option to subdivide further using subclasses. The three classes are Reserves, Contingent Resources, and Prospective R esources. Separately, the range of uncertainty in the estimated recoverable sales quantities from that specific project is categorized based on the principle of capturing at least three estimates of the potential outcome: low, best, and high estimates. For projects that satisfy the requirements for commerciality, Reserves may be assigned to the project, and the three estimates of the recoverable sales quantities are designated as 1P (proved), 2P (proved plus probable), and 3P (proved plus probable plus possible) Reserves. The equivalent categories for projects with contingent resources are 1C, 2C, and 3C, while the terms low estimate, best estimate, and high estimate are used for prospective resources. The system also accommodates the ability to categorize and report reserve quantities incrementally as proved, probable, and possible, rather than using the physically realizable scenarios of 1P, 2P, and 3P. The two terms, project classification and reserve/resource categorization do not overlap. So for instance, possible reserves cannot reflect uncertainty in maturation of the project of development. In this case, a contingent resource would be the appropriate classification. Project definition PRMS is a project-based system, where a project: represents the link between the petroleum accumulation and the decision-making process, including budget allocation. A project may, for example, constitute the development of a single reservoir or field, or an incremental development in a producing field, or the integrated development of a group of several fields and associated facilities with a common ownership. In general, an individual project will represent a specific maturity level at which a decision is made on whether or not to proceed (i.e., spend money), and there should be an associated range of estimated recoverable resources for that project.” A project may be considered as an investment opportunity. Management decisions reflect the selection or rejection of investment opportunities from a portfolio based on consideration of the total funds available, the cost of the specific investment, and the expected outcome (in terms of value) of that investment. The project is characterized by the investment costs (i.e., on what the money will actually be spent) and provides

Figure 2. Classification of resources across a fault boundary. In the absence of a discovery well in the hanging wall (right), the data available and the uncertainty determine the resource classification.

the fundamental basis for portfolio management and decision making. In some cases, projects are implemented strictly on the basis of strategic drivers but are nonetheless defined by these financial metrics. The critical point is the linkage between the decision to proceed with a project and the estimated future recoverable quantities associated with that project. A project may involve the development of a single petroleum accumulation, or a group of accumulations, or there may be more than one project implemented on a single accumulation. The following are some examples of projects: • Where a detailed development plan is prepared for partner and/or government approval, the plan itself defines the project. If the plan includes some optional wells that are not subject to a further capital commitment decision and/ or government approval, these would not constitute a separate project, but would form part of the assessment of the range of uncertainty in potentially recoverable quantities from the project. • Where a development project is defined to produce oil from an accumulation that also contains a significant gas cap and the gas cap development is not an integral part of the oil development, a separate gas development project should also be defined, even if there is currently no gas market. • Where a development plan is based on primary recovery only, and a secondary recovery process is envisaged but will be subject to a separate capital commitment decision and/ or approval process at the appropriate time, it should be considered as two separate projects.
September 2012 The Leading Edge 1029

Geophysics in reserves estim ation

• Where decision making is entirely on a well-by-well basis, as may be the case in mature onshore environments, and there is no overall defined development plan or any capital commitment beyond the current well, each well constitutes a separate project. In the assessment of an undrilled prospect, a risked economic evaluation will be made to underpin the decision whether to drill. This evaluation must include consideration of a conceptual development plan in order to derive cost estimates and theoretically recoverable quantities (Prospective Resources) on the basis of an assumed successful outcome from the exploration well. The project is defined by the exploration well and the conceptual development plan. In some cases, an investment decision may be requested of management that involves a combination of exploration, appraisal, and/or development activities. Because PRMS subdivides resource quantities on the basis of three main classes that reflect the distinction between these activities (i.e., Reserves, Contingent Resources, and Prospective Resources), it is appropriate in such cases to consider that the investment decision is based on implementing a group of projects, whereby each project can fit uniquely into one of the three classes. Project classification Under PRMS, each project must be classified individually so that the estimated recoverable sales quantities associated with that project can be correctly assigned to one of the three main classes: Reserves, Contingent Resources, or Prospective Resources (Figure 1). The distinction between the three classes is based on the definitions of (a) discovery and (b) commerciality The term “discovery” is used for a petroleum accumulation, or several petroleum accumulations collectively, whose existence has been determined by its actual penetration by a well, which has also clearly demonstrated the existence of significant moveable petroleum by flow to the surface or at least some recovery of a sample of petroleum. Log or core data may suffice for proof of existence of moveable petroleum if an analogous reservoir is available for comparison. The definition remains completely independent from any considerations of commerciality. In this context, “significant” implies that there is evidence of a sufficient quantity of petroleum to justify estimating the in-place volume demonstrated by the well(s) and for evaluating the potential for economic recovery. Estimated recoverable quantities from a discovery are classified as Contingent Resources until such time that a defined project can be shown to have satisfied all the criteria necessary to reclassify some or all of the quantities as reserves. In cases where the discovery is, for example, adjacent to existing infrastructure with sufficient excess capacity, and a commercially viable development project is immediately evident (i.e., by tying the discovery well into the available infrastructure), the estimated recoverable quantities may be classified as Reserves immediately. More commonly, the estimated recoverable quantities for a new discovery will be classified as Contingent Resources while further appraisal and/or evaluation is carried
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Figure 3. Two fault blocks separated by one fault. An oil producer is tapping into block A. It is unknown what the sealing characteristics of the fault are. Reserves may be assigned in block A. Without a well penetration in block B and clear evidence regarding fault seal, potential volumes are classified as prospective resources.

out. In-place quantities in a discovered accumulation that are not currently technically recoverable may be classified as Discovered Unrecoverable. A project is deemed commercial if the degree of commitment is such that the accumulation is expected to be developed and placed on production within a reasonable time frame. A reasonable time frame for the initiation of development depends on the specific circumstances but, in general, should be limited to around five years. The criteria for commerciality (and hence assigning reserves to a project) should be considered with care and circumspection. While estimates of reserve quantities will frequently change with time, including during the period before production start-up, it should be a rare event for a project that had been assigned to the Reserves class to subsequently be reclassified as having Contingent Resources. Such a reclassification may occur as the consequence of an unforeseeable force majeure event that is beyond the control of the company, such as an unexpected political or legal change that causes development activities to be delayed beyond a reasonable time frame. A significant negative change in market prices may also cause such a reclassification. As an example of resource classification, consider two fault blocks separated with different size of fault offsets and different data sets (Figure 2). The left fault block has discovered hydrocarbons and development or planned development. The resources are thus Proved Reserves. If the fault is sealing or the fault is completely offsetting the reservoir, then the right fault block has Prospective Resources (case 1). If the fault is non-sealing or the reservoir is not completely offset, then the resource classification depends on the other information available. In case 2, seismic, geologic, pressure, and analog data indicate extension and continuity of reservoir. The development plan also includes the right fault block but the resources are Unproved Reserves. In case 3, the development plan does not include the right fault block. Therefore, the resources are Contingent. Finally, in case 4, there are unknowns about reservoir or hydrocarbon presence, and, therefore, the resources in the right fault block are Prospective Resources.

Geophysics in reserves estim ation

Figure 4. Two four-way structures sit in the same trend and have the same reservoir and petroleum system. A gas discovery well was drilled over the structure to the right. If seismic amplitude analysis shows that the gas cap can reliably be defined over the structure and consistent with the well and production tests, then the technology could be used to infer a similar gas cap over the structure to the left.

Uncertainty categorization The “range of uncertainty” (see Figure 1) reflects a range of estimated quantities potentially recoverable from an accumulation (or group of accumulations) by a specific, defined, project. Because all potentially recoverable quantities are estimates that are based on assumptions regarding future reservoir performance (among other things), there will always be some uncertainty in the estimate of the recoverable quantity resulting from the implementation of a specific project. In almost all cases, there will be significant uncertainty in both the estimated in-place quantities and in the recovery efficiency, and there may also be project-specific commercial issues. In PRMS, the range of uncertainty is characterized by three specific scenarios reflecting low, best, and high case outcomes from the project. The terminology is different depending on which class is appropriate for the project, but the underlying principle is the same regardless of the level of maturity. In summary, if the project satisfies all the criteria for reserves, the low, best, and high estimates are designated as proved (1P), proved plus probable (2P), and proved plus probable plus possible (3P), respectively. The equivalent terms for contingent resources are 1C, 2C, and 3C, while the terms “low estimate,” “best estimate,” and “high estimate” are used for prospective resources. The three estimates may be based on deterministic methods or on probabilistic methods. The relationship between the two approaches is highlighted in PRMS with the statement that: “A deterministic estimate is a single discrete scenario within a range of outcomes that could be derived by probabilistic analysis.” Further, “uncertainty in resource esti-

mates is best communicated by reporting a range of potential results. However, if it is required to report a single representative result, the “best estimate” is considered the most realistic assessment of recoverable quantities. It is generally considered to represent the sum of proved and probable estimates (2P) when using the deterministic scenario or the probabilistic assessment methods.” Examples of the use of seismic data The above discussion on petroleum resources, project definition and classification, and uncertainty categorization has been general and concerns all project and data types. Two examples here illustrate how the use of seismic data can add value to the reserves and resource process. Like other data types, seismic data come at some expense, and the seismic data must therefore compliment and compete with other data on technical justifications, cost and value of information (VOI). The advantage of the new SEC rules for reserves estimation is that as a reliable technology, seismic data can now directly influence the reserves estimation and classification, and, therefore, the potential value of seismic data and interpretation is increased. The key words are “reliable technology.” For the two examples shown here it is assumed that the technologies are proven reliable for the particular case in question. Therefore, the examples show the value of seismic data, if the data are reliable. Consequently, if there is doubt whether the data are going to prove reliable one might factor this uncertainty into the data feasibility study. For discussion on the reliability of seismic technology refer, the reader is referred to the article by Kloosterman et al. in this special section.
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In the first example, we will look at the information gained and from the use of 4D seismic data to monitor oil production from a horizontal well in a fault block (Figure 3). A fault has been identified in proximity to the oil producer. The reservoir is not fully offset by the fault and there is doubt about the fault transmissibility in this area. If the fault is sealing, then continuity of productivity is restricted to fault block (A). Reserves have thus been assigned to the estimated oil that can be produced in a reasonable time frame by wells in block A. The 1P, 2P, and 3P reserves are then estimated based on the certainty estimates around the well and the reservoir model in this block only. The volumes estimated in block B are undiscovered Prospective Resources, because there is no well in the block and there is no confirmation of the presence of oil-bearing reservoir. If 4D seismic data were acquired, and the data showed production effects directly up to and stopped at the fault boundary, then the fault could be assumed sealing. The resources in block B would thus stay as undiscovered until a well could be sanctioned and drilled to test presence of a productive reservoir. The prospective resources may move directly to reserves status with 1P, 2P, and 3P estimates being governed by the new well information and the reservoir model in block B (assuming that other commercial criteria are satisfied). If, however, the 4D seismic showed that oil was being produced clearly across the boundary, then the fault could be assumed transmissible. Some or all of the recoverable volumes in block B could thus immediately be included in 3P (or perhaps 2P) reserves as extensions to the reservoir discovered by the existing well in block A. The value of the 4D seismic data over this particular area is therefore related to the ability to increase the reserves directly or by planning and sanctioning an extra well in the other fault block. The second example shows the use and value of seismic amplitude interpretation over a gas field or gas discovery. In Figure 4 there are two four-way closures within the same stratigraphic trend and close to each other. The petroleum system is considered the same and the reservoir geology likewise. A gas discovery well has been drilled over structure A. If development wells either have been drilled or are sanctioned to be drilled over the gas discovery along with necessary production and transportation facilities, then appropriate reserves categories can be assigned. Naturally, structure B would be considered interesting to drill and develop. Because no wells are drilled over structure B, it can be classified only as a prospect with low, best, and high estimation of prospective resources. Under the old SEC rules, the resources would remain prospective until a discovery well was drilled and tested over the structure. Under the new SEC rules, the Prospective Resources could be reclassified as possible reserves if reliable technology can show with high confidence that the structure contains gas, as long as the structure sits on the same geological trend and share petroleum system. If seismic interpretation of a data set over structure A with the discovery well can show the full extent of the gas and assuming the technology could be considered reliable, then the same interpretation could be tried over structure B. In this example, it is assumed that the original discovery is in a group of accumulations. In
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an alternative version, the gas discovery well over structure B has been drilled already but the gas down to contact is not known. Probable and possible reserves could be assigned below the lowest gas penetration based on seismic amplitude interpretation tied to structure A data. Conclusions Oil and gas reserves are closely tied to the projects in place or being planned and sanctioned that will produce the hydrocarbons. Resources are classified in three ways: as Prospective Resources, as Contingent Resources, and as Reserves. The classification reflects the status of the technical and commercial maturity. Prospective Resources are those that have yet to be discovered. Contingent Resources are those discovered quantities that could be produced if an appropriate production plan can be sanctioned. Reserves are those commercial resource quantities that can be produced under a current development plan and within a reasonable time frame. The value of seismic data in reserves estimation is its ability to increase the chance of commerciality or to reduce the uncertainty in the resource and reserves estimation. The information gathered from seismic interpretation can be in reservoir presence and quality discrimination, in fluid distribution and contacts, or in dynamic behavior of the reservoir during production. When evaluating options to acquire or use seismic data, then the cost of the seismic data can be measured against the value gained on defining commerciality and reducing resource uncertainty. Should there be doubt about the success of the seismic interpretation, then the value of the seismic data can be weighted by its influence on the chance of success estimates. Ultimately, to use seismic or any other geophysical data for reserves estimation, the technology needs to be reliable, which is the responsibility of the individual companies to demonstrate.
References
Guidelines for Application of the Petroleum Resources Management System, PRMS-AG, November 2011, http://www.spe.org/industry/reserves.php. Modernization of Oil and Gas Reporting; Final Rule, 2009, http:// www.sec.gov/rules/final/2009/33-8995fr.pdf.

Acknowledgment: The part of this article discussing the general PRMS concepts is based Chapter 2 of the PRMS-AG (2011), written by James G. Ross with contributions from many individuals. We thank SPE for permission to use this material for the present article. Corresponding author: [email protected]

Geophysics in reserves estim ation

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

The role of geophysics in petroleum resources estimation and classification—new industry guidance and best practices
Henk Jaap Kloosterman and Pierre-Louis Pichon, SEG Oil & Gas Reserves Committee

eophysical technologies can have an impact on both the uncertainty axis as well as the maturity axis of the resource classification framework described in the Petroleum Resources Management System (PRMS). For conventional resources, the technical maturity of a project can be significantly impacted by applying geophysical technologies for assessing structural definition, reservoir development, fluid contacts and movements, and for flow surveillance. For unconventional resources, the largest uncertainties to date are on efficient extraction, an area where geophysical technologies have so far mainly played a supporting role. Geophysical technologies also may contribute to the definition of the range of the low, best, and high resource volume estimates. The SEG-sponsored document “Guidelines for Application of the Petroleum Resources Management System (PRMS-AG)” makes many references to the application of geophysical technologies on estimating petroleum resource volumes. This paper describes the most relevant geophysical parts of the PRMS-AG and key issues for the TLE readership. Relevant sections from the PRMS-AG are quoted in italics. For further reference to an overview of the PRMS, the reader is referred to the article by Lorenzen et al. in this special section and to the PRMS-AG document itself (http://www.spe.org/industry/reserves/php). Key areas where seismic technologies impact resource estimation Faulted reservoirs. The role of seismic technologies in determining the trap geometry of a field is described in PRMS-AG sec-

G

tion 3.2.1. Particular attention is devoted to faulted reservoirs: “For fields interpreted to be faulted, it may be necessary to classify resource estimates differently for individual fault blocks. It is important to make a distinction whether the fault that separates the undrilled fault block from a drilled fault block can be considered a major, potentially sealing, fault or not.” Figure 1 highlights some considerations for resource classification for undrilled fault blocks. As can be seen in Figure 1, seismic attributes can be used as part of integrated analysis to assess the likelihood of economically producible reservoir in the undrilled fault block. In PRMS-AG section 3.2.2, the following guidance is given: Seismic amplitude anomalies may also be used to support reservoir and fluid continuity across faulted reservoir provided that the following conditions are met: • Within the drilled fault block, well logs, pressure, fluid data and test data demonstrate a strong tie between the hydrocarbon-bearing reservoir and the seismic anomaly. • Fault throw is less than reservoir thickness over (part of ) the hydrocarbon bearing section across the fault and the fault is not considered to be a major, potentially sealing, fault. • The seismic flat-spot or the seismic anomaly is spatially continuous and at the same depth across the fault.

If these conditions are met, the presence of hydrocarbon in the adjacent fault block above the seismic flat-spot or seismic amplitude anomaly may be judged sufficiently robust to qualify the hydrocarbon volumes as within the same known accumulation and thus qualify as reserves. If these conditions are only partially met, the interpreter must consider the increased level of uncertainty inherent in the data and appropriately classify the volumes based on the uncertainty components. Caution should be exercised in assigning reserves and resource classification categories. The levels of risk and uncertainty should be commensurate the quality of the data, velocity uncertainty, repeatability, and quality of supporting data. In the example shown in Figure 2, a seismic amplitude anomaly has been calibrated to a hydrocarbon-bearing Figure 1. Considerations for resource classification for undrilled fault blocks.
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Geophysics in reserves estim ation

Figure 2. Example of resource classification for undrilled fault blocks.

Figure 3. Considerations for resource classification for undrilled fault blocks.

reservoir by well D. The drilled area is separated by faults from three other fault blocks to be targeted by development wells A, B, and C. Which type of resources can be associated with those undrilled blocks depends on the nature of the fault as well as the outcome of the integrated analysis on the likelihood of hydraulic communication across the fault. However, before doing any seismic attribute analysis in

faulted reservoirs, it is important to start off with mapping the faults in 3D with sufficient detail. In the example shown in Figure 3, it is the detailed mapping of the faults that provided evidence for reservoir continuity across the fault relayramp, impacting the likelihood assessment of economically producible reservoir in undrilled fault block, and hence the resource classification.
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Prediction of rock and fluid properties. Seismic technology can be used to predict the rock and pore-fluid properties of the reservoir and sometimes its pressure regime. In PRMS-AG section 3.2.2, the following guidance is given: “The reservoir properties that 3D seismic can potentially predict under suitable conditions are porosity, lithology, presence of gas/ oil saturation as well as pressure.” The section gives a comprehensive overview on the seismic data quality and analysis requirements for such analysis. In the deep-water oil field example in Figure 4, the oil accumulation is trapped against a fault to the northeast dipping to an oil-water contact (OWC) to the southwest. The seismic amplitude maps are from a near-offset (left) and far-offset (right) volume. The oil-water contact appears as an amplitude increase on the near offsets and an amplitude decrease on the far offsets. Both run along a structural contour. The seismic amplitude response is consistent with the trap geometry, the depositional model, and the seismic rock properties from the well data. The PRMS-AG also provides indication of the limitation of the seismic methodologies: It is usually not possible to distinguish a fully saturated gas accumulation from a partially saturated column (residual gas) using full stack or conventional (two-term) AVO analysis so this may remain as an unresolved risk. Direct estimation of density contrast using higher order AVO analysis can in principle distinguish between the two but this is an emerging technology and would need to be supported by a historical track record. It is noted that in many other examples, in which the seismic evidence itself is not as convincing, other data sources (e.g., pressure data, performance data, geologic deposition model) will also contribute as part of an integrated analysis to achieve comparable confidence of the recoverable volumes below the Lowest Known Hydrocarbons (LKH), as observed in the wells.

When a known hydrocarbon accumulation is being appraised, seismic flat-spots and/or seismic amplitude anomalies can be used to increase confidence in fluid contacts when the following conditions are met: • The flat-spot and/or seismic amplitude anomaly is clearly visible in the 3D seismic, and not related to imaging issues. • Within a single fault block, well logs, pressure, and well test and/or performance data demonstrate a strong tie between the calculated hydrocarbon/water contact (not necessarily drilled) and the seismic flat-spot and/or downdip edge of the seismic anomaly. • The spatial mapping of the flat-spot and/or downdip edge of the amplitude anomaly within the reservoir fairway fits a structural contour, which usually will be the downdip limit of the accumulation.

Figure 4. Example of seismic amplitudes to predict fluid properties.

Figure 5. Example of reliability of seismic amplitudes to predict rock and fluid properties.
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Geophysics in reserves estim ation

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Geophysics in reserves estim ation

When using seismic attributes to predict reservoir development and/or pore fill, it is important to gather evidence on the reliability of the seismic data. An assessment of the quality of the seismic data, combined with seismic modeling predictions as well as the analysis of the historic track record are key ingredients to this. In the example in Figure 5, high seismic amplitudes could be directly linked to stacked channel belts; 16 exploration and appraisal wells, penetrating 40 sands in the same geological setting, were all predicted correctly. The results of one well are shown; the C2 and H zones encountered hydrocarbon-bearing sands but the D zone did not see any reservoir development, as predicted based on predrill seismic modeling. There is an inherent uncertainty in predicting reservoir properties from 3D seismic. The PRMS-AG states in section 3.4 on seismic inversion usage for predicting rock and fluid properties: Inversion requires the seismic to be combined with additional data and hence good quality impedance inverted volumes will contain more information than a conventional seismic volume Specifically, additional data are required to compensate for the lack of low frequencies in the seismic. However there will rarely be enough data to fully constrain the low frequency component so inversion results will be non-unique. Because of this uncertainty a probabilistic approach can be followed to try to capture the full range of possible outcomes. The uncertainty analysis should cover the non-uniqueness of the inversion process and the uncertainties arising from the rock property model. The probabilities of the various outcomes can then subsequently be used as input to Reserves and Resource volume assessments. An example of probabilistic seismic inversion is given in Figure 6. In this example, the key uncertainty for estimation of in-place volumes is the distribution of net sand thickness. The low, mid, and high net sand maps are the output of a probabilistic seismic inversion. Each map fits the well data used to constrain the model. The three net sand maps reflect the uncertainty in the net sand distribution and can be used to constrain three different “oil-in-place” scenarios in low, mid and high case static models that can be carried through to reservoir simulation and are thus key input to the resource volume assessment and classification. Reservoir surveillance. 3D seismic analysis can be used to monitor changes over time in pore-space composition, pressure, and temperature with fluid movement in the reservoir. This application is often called time-lapse seismic or, more commonly, as 4D seismic. PRMS-AG section 3.2.3 gives more details on the geophysical aspects to be taken into account. Time-lapse seismology impacts estimation of resources and reserves in various ways. For example, bypassed oil reserves can be spotted on time-lapse seismic when a compartment (fault block or other discrete component of the trap) is identified by timelapse seismic as an isolated pool that previously was believed to be part of the field’s connected pool or pools. An example to illustrate this is presented in Figure 7, where time-lapse seismic results revealed an area in the west of the F block without 4D sweep that differed from what was expected.
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New spectrally boosted 3D seismic also shows evidence for a (newly mapped) normal fault cutting the F block into two separate blocks. The new fault was incorporated in the model update, allowing an improved history match by adjusting the fault seal properties. As a result of incorporating the time-lapse seismic results, the bypassed volumes in the southwestern part of block F will have to be reclassified from “developed reserves” to “contingent resources” until further development activities mature. Direct detection of the original versus current depth of the oil/water contact (OWC) in a producing field is easier on a time-lapse seismic data set than on a single data set because changes of saturation in the interval swept by the water can noticeably alter the acoustic/elastic impedance of this part of the reservoir. This impedance change can be detected by time-lapse seismic comparisons. An example of this is given in Figure 8. These OWC changes, as derived from time-lapse seismic results, can subsequently be mapped out laterally and be used to update the static and dynamic reservoir models that underpin the resources and reserves volumes estimate. In the bitumen field example in Figure 9, steam-assisted gravity drainage (SAGD) is the in-situ thermal recovery method being used to develop, with a pattern of horizontal well pairs, some 20 billion barrels of bitumen approximately 400 m below the surface. The acoustic properties of bitumen sands exhibit a strong response to temperature changes, resulting in a significant velocity decrease through zones in the reservoir which have

Figure 6. Use of probabilistic seismic inversion as input to resource volume estimation.

Figure 7. Time-lapse seismic results indicate the presence of a sealing fault.

Geophysics in reserves estim ation

Figure 8. Example of using time-lapse seismic to assess OWC movement.

Figure 9. Example of using time-lapse seismic to monitor a steam flood.

been thermally altered by the SAGD process. This unique response makes it possible to use time-lapse seismic methods to monitor the thermal evolution of the steam over time. Time-lapse history-matched models have been used to underpin reserves and resource volume estimates. Uncertainty in seismic predictions Geophysical technologies may contribute to the definition of the range of the low, best, and high resource volume estimates. The PRMS-AG states in section 3.3 on uncertainty for seismic based predictions: Predictions from 3D seismic data aimed at defining trap geometry, rock/fluid properties or fluid flow have an inherent uncertainty. The accuracy of a given seismic based prediction is fundamentally dependent on the resulting interplay between: • The quality of the seismic data (bandwidth, frequency

content, signal-to-noise ratio, acquisition and processing parameters, overburden effects, etc.). • The uncertainty in the rock and fluid properties and the quality of the reservoir model used to tie subsurface control to the 3D seismic volume. It is important to realize that this uncertainty assessment will need to be kept evergreen and needs to be revisited when new data become available. The PRMS-AG provides some guidance on this: A derived reservoir model that is accurately predicting a subsurface parameter or process as proven by drilling results from new wells has demonstrated a reduction in uncertainty and the current level of uncertainty can be revised accordingly after several successful predictions. Such a reservoir model is far more valuable than an untested reservoir model, even though the latter may be more sophisticated. Care should be taken exSeptember 2012 The Leading Edge 1039

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Figure 11. Resource triangle.

Figure 10. Example of an integrated workflow underpinning resource estimates.

or stratigraphic condition (typically with each accumulation bounded by a down-dip contact with an aquifer) that is significantly affected by hydrodynamic influences such as the buoyancy of petroleum in water. The petroleum is recovered through wellbores and typically requires minimal processing prior to sale. • Unconventional resources exist in hydrocarbon accumulations that are pervasive throughout a large area and that are generally not significantly affected by hydrodynamic influences (also called “continuous-type deposits”). Such accumulations require specialized extraction technology, and the raw production may require significant processing prior to sale. To date, the largest uncertainties on unconventional resources are on efficient extraction process. Geophysical technology has so far mainly played a supporting role, below is a summary of the main applications to date: • In-situ bitumen: High-resolution seismic is used to guide bitumen extent. • Tight gas formation (TGF): Seismic velocities provide guidance on porosity distribution whereas wide-azimuth seismic acquisition and seismic velocity (azimuthal) anisotropy analysis provide guidance for fracture distribution. • Coal bed methane (CBM): Seismic may be considered to identify high-permeability zones. • Shale gas: Microseismic can monitor stimulations, understand fracture geometries. 3D seismic data may demonstrate shale continuity and character away from well control and assess risk of sealing faults. Further guidance is desirable for the application of geophysical technology for unconventional resources, in particular in view of the materiality of these resource volumes.
Corresponding author: [email protected]

trapolating the results from new wells, if such programs targeted high amplitude or “sweet spot” and remaining targets are not in a similar setting. Appropriate consideration should be made regarding predictability. It is useful to assess the track record of a given 3D seismic volume or of regional analogues in predicting subsurface parameters at new well locations before drilling. The predictive record is the best indicator of the degree of confidence with which one can employ the seismic to estimate reserves and resources as exploration and development proceeds in an area. Furthermore, the geophysical input into the definition of the low, best, and high case resource volume estimates has to be an integral part of an integrated workflow (Figure 10). In this example, seismic analysis was instrumental in defining the reservoir and establishing internal reservoir continuity. The seismic analysis results were calibrated and integrated with log, core, and well data underpinning the static and dynamic models that formed the foundation of the reserves estimates. Unconventional resources It is important to make the distinction between two types of petroleum resources that may require different approaches for their evaluations. In PRMS-AG section 8, describes this as follows: • Conventional resources exist in discrete petroleum accumulations related to a localized geological structural feature and/
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Geophysics in reserves estim ation

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

Resource assessment based on 4D seismic and inversion at Ringhorne Field, Norwegian North Sea
David H. Johnston, ExxonMobil Production Company Bernard P. Laugier, ExxonMobil Exploration and Production Norway A/S

ime-lapse (4D) seismic data from Ringhorne Field in the North Sea are used to monitor water movement in both Paleocene- and Jurassic-age reservoir sands, improve existing geologic and simulation models, and enable more cost-effective field operations. The structural complexity of the reservoirs, their proximity to the high-impedance Cretaceous chalk, and a relatively small 4D response has required a significant effort in seismic acquisition and processing which resulted in highly repeatable surveys (Johnston et al., 2010). In addition to the 4D interpretation, VP/VS derived from simultaneous elastic inversion is diagnostic of sand and provides additional constraints on Ringhorne subsurface models. Connected volumes based on VP/VS correlate to areas of water sweep seen in the 4D data and reduce uncertainty in 4D interpretation. Relative P-wave impedance changes calculated from inversion are consistent with presurvey 4D predictions. The 4D seismic data and inversion help explain water breakthrough timing, improve our understanding of field production history, and have resulted in the identification of additional infill well opportunities. The first 4D monitor survey at Ringhorne and the results of inversion also triggered an update to the volumetric assessment of the Paleocene Ty reservoir. Prior to the acquisition of the survey in 2006, the key uncertainties in resource estimation were effective reservoir thickness and area above the oilwater contact. Much of the sand thickness is below seismic resolution. Added complexities are that the reservoir is nearly transparent on preproduction stacked seismic data and there is seismic interference from the underlying strong top chalk reflection. The nearby flat areas of the reservoir in the north and south of the field are particularly sensitive to depth conversion, adding to uncertainty in volumetrics.

T

Production data suggested a larger in-place volume than had been previously modeled. The 4D survey showed water sweep where no sand had been previously interpreted. This resulted in an expanded northern flank of the reservoir. The downdip limit of the water sweep, as defined by the 4D image, indicates the position of the original oil-water contact. This provided the justification to lift the reservoir in places. In addition, evidence from the 4D data suggested increased sand thickness in areas and the pinch out of the reservoir against the chalk was moved updip as a result of the inversion. As a result of the reassessment, the resource base for the Ty reservoir was increased by 40%. Introduction Time-lapse (4D) seismic data provide an opportunity to identify and quantify changes in reservoir properties that occur during hydrocarbon production. 4D seismic has been extensively used to identify areas of bypassed and undrained oil, improve the existing geologic model, and enable more cost-effective field operations. In this paper, we show that 4D seismic data, combined with improved 3D imaging and elastic inversion, can also provide valuable constraints on resource evaluation. Ringhorne Field is in Production License 027 (100% ExxonMobil interest), offshore Norway in the North Sea (Figure 1). The Ringhorne Jurassic and Ringhorne West (Ty) reservoirs were discovered in 1997 and 2003, respectively, with first production occurring in 2003. The Lower Jurassic fluvial and shallow Statfjord reservoirs form in a structural horst-block trap on a basement high and are produced by a combination of natural water drive and water injection. The deep-water Paleocene Ty sands are the subject of this paper. They stratigraphically pinch out onto the Ringhorne high at a depth of about 1900 m subsea (Figure 2a). Ty is a low-reflectivity, single-seismic cycle, subtuning-thickness reservoir which directly overlays the regionally extensive highimpedance Cretaceous chalk. It is evident from Figure 2b that the chalk dominates the reflectivity in the seismic section. The sands are about 85% net-to-gross, with an average porosity of 30% and multidarcy permeability. They are being depleted by a strong natural water drive. The light oil (API 39) has similar properties and is in pressure communication with the Jurassic oil reservoirs. A 4D feasibility study prior to acquiring the monitor survey suggested that impedance changes resulting from water
Editor's note: This article was expanded by the authors from "New opportunities from 4D seismic and lithology prediction at Ringhorne field, Norwegian North Sea," SEG Expanded Abstracts 29, 4160 (2010), doi: http://dx.doi.org/10.1190/1.3513732.

Figure 1. Location of Ringhorne Field.
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Figure 2. Ringhorne Field cross sections. (a) Geologic section showing the Jurassic Statfjord reservoirs in a horst-block structural trap and the Ringhorne West Paleocene Ty sands pinching out against the Cretaceous chalk. (b) Seismic section. The reflectivity is dominated by the highimpedance chalk and the Ty sand is seismically transparent.

Figure 3. Seismic programs for the Ringhorne area. The 2006 4D monitor survey was designed to cover Ringhorne and Ringhorne West fields (RH and RHW). Ringhorne East (RHE) had just come on production. The 2001 baseline and 2009 and 2012 4D surveys cover all of the fields in the area.

displacing oil would be 7–8%. While above what is normally considered the detectable limit for 4D detection, the proximity of the reservoirs to the chalk added complications. A relatively low-amplitude change of 20% (relative to the chalk reflectivity) and side-lobe interference from the chalk reflection required that the 4D data be highly repeatable. The baseline 3D seismic survey, encompassing both Ringhorne and Balder fields, was acquired in 2001 (Figure 3). The first monitor survey, covering Ringhorne West and Jurassic, was shot in 2006 after three years of production. The second 4D survey, acquired in 2009, covers Ringhorne, Balder, and the nearby Ringhorne East and Forseti fields. A third repeat survey was shot in 2012. All 4D surveys employed common acquisition strategies such as matching baseline source positions, overlapping streamers to improve coverage, infill data to optimize repeatability, and feather matching where possible. A postsurvey assessment of the 2006 monitor survey demonstrated that geometrical repeatability goals were achieved and that a median dS + dR < 20 m was obtained for all offsets. Both base and monitor surveys were coprocessed to
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maximize repeatability and to preserve and resolve differences because of production. The mode of the nrms difference calculated over a window encompassing the chalk is 7%, making this survey one of the most repeatable ever acquired by ExxonMobil. At the time of the 2006 monitor survey, the Ty sand had three wells on production. Two wells (C09 and C10 in Figure 4a) had not seen water. However, the reservoir simulation model, history-matched through 2005, predicted water to be encroaching both wells C09 and C10 with breakthrough at the end of the second quarter of 2007. The modeled sweep is illustrated by the 4D difference predicted by simulationto-seismic modeling run before the monitor survey was shot (Figure 4b). The southernmost well (C07) had water breakthrough, but its water-cut increase was slower than predicted by the simulation model. Overall production by 2006 suggested greater in-place volumes than originally estimated for the Ty reservoir. Ty shares a common original oil-water contact (OOWC) with the Jurassic sands at 1917.5 m subsea. However, several key uncertainties effected resource evaluation prior to the 4D program. The reservoir is thin and much of it is below seismic resolution. It is also relatively transparent on stacked seismic data. This made interpretation of the top sand challenging. It was also difficult to establish the updip limit of the reservoir. As a result, there was uncertainty in the volume of attic oil above the producers. And finally, the flat areas to the north and south of the field are sensitive to depth conversion. The well control is at the reservoir crest and there is little downdip depth calibration. 4D interpretation The initial 4D interpretation was based on quadrature-phase difference data. The quadrature transformation concentrates the difference energy in the reservoir interval, facilitating volume-based interpretation methods and 4D well ties. It is most suited for reservoirs with a thickness at about seismic tuning (the case for the Ty sand). Positive values of the quadrature-phase difference correspond to increases in seismic impedance, expected where water displaces oil. Connected geobodies derived from the 4D difference are then used to map and evaluate the volume of water sweep. The 4D seismic geobody for water sweep in the Ty sand is illustrated in Figure 5. It shows a generally broad and uniform water sweep along the flank of the reservoir from the original oil-water contact (OWC), defined by the left edge of the geobody, to the water front, delineated by the right edge. The data imply that there is good communication within the Ty sand with only minor EW-oriented baffles that might segment the reservoir (Figure 6a). The position of the originally modeled OOWC implies that the Ty reservoir, which is difficult to image on P-wave seismic data, was initially interpreted too deep on the west flank as illustrated in Figure 6b. The true OOWC, defined by the downdip limit of the 4D geobody, also provides a strong constraint on time-depth conversion, particularly in the northern part of the field. The extent of the 4D swept volume
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Figure 4. Ringhorne West. (a) Structural map of the base Ty reservoir showing the three production wells, OOWC, and the interpreted updip pinch out of the sand. (b) Predicted 4D difference based on simulation to seismic modeling. The simulation shows water encroaching on all three wells.

outside the modeled OOWC is a clear indication of reservoir volumes not included in the initial assessment. The 4D interpretation is validated by comparing the swept volume calculated from the 4D response to the cumulative liquid production from the three wells at the time of the 4D survey acquisition. Porosity, net-to-gross, water-saturation curves, and recovery factor are taken from the geologic and simulation models. The geobody volume is also corrected at its downdip and updip locations for water sweep that falls below seismic resolution. Changes to the geobody are then validated by 4D modeling to ensure that they are consistent with the data. The result is a good match to the 33 million barrels of oil produced by mid 2006. The 4D results are also consistent with the production data which has water breakthrough only at well C07. The data suggest water is fingering to a limited entry point, explaining the slow rate of water-cut increase. The data also suggest that breakthrough at wells C09 and C10 would be later than predicted by the simulation model. At the time of the 2006 monitor survey, the sweep had reached a major NSoriented fault which acted as a ledge, holding back the lateral movement of the water. By the time of the 2009 survey acquisition, water had risen and spilled over the fault, breaking through to well C10. 3D inversion of the 2001 baseline data Well-log data suggest that VP/VS information can help map sand in the Ty reservoir. Thus, near-, mid-, and far-angle stacks from the 2001 preproduction baseline survey were simultaneously inverted for P- and S-wave impedance using a constrained sparse-spike inversion algorithm. The constraints were incorporated by limiting the impedance estimates within some intervals and by following trends

Geophysics in reserves estim ation

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Figure 5. Water sweep geobody, extracted from the quadrature-phase 4D difference, on the base Ty time surface. Also shown are the pre-4D interpreted OOWC (light blue) and updip pinch out (red). The revised pinch-out interpretation is shown in purple. Note that the 4D response extends outside the initial OOWC interpretation.

defined by impedance logs. Incidence angles range between 5 and 17.5° for the near-angle stack, between 17.5 and 27.5° for the mid-angle stack, and between 27.5 and 40° for the far-angle stack. Welllog data from six wells and six horizons were used to constrain the initial Earth model and wavelets for each angle stack were estimated from three vertical wells with sonic and density logs. Simultaneous elastic inversion was also applied to the time-lapse data using workflows discussed by Sarkar et al. (2003) which minimize artifacts associated with the inherent nonuniqueness of inversion. One approach is to use a common initial Earth model that is already a good representation of the subsurface for both base and monitor inversions. The second approach is to directly invert the seismic differences in order to obtain the changes in the elastic parameters. Both workflows were used at Ringhorne with no significant differences in results. However, as discussed by Gouveia et al. (2004), strong constraints on the inversion are needed to minimize noise. The results of the inversion provided a quantitative estimate of impedance change that validated our rock physics models of the Ringhorne reservoirs but did not add substantially to the interpretation of water sweep over the quadrature difference results.

Figure 6. Cross sections from the quadrature-phase 4D difference volume. (a) Strike section through the water sweep geobody shown in Figure 5. (b) Dip section showing the position of the original top Ty interpretation relative to the 4D response.
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Figure 7. P- and S-wave impedance and VP/VS derived from simultaneous elastic inversion of the 2001 baseline seismic data.

Figure 8. 2001 full-stack data, reprocessed as part of the 4D program. Changes in chalk amplitude correspond to Ty sand presence.

Figure 7 shows a cross section of P-impedance, S-impedance, and VP/VS estimated from simultaneous inversion of the 2001 baseline seismic data. The Ty sands, transparent on the P-wave impedance volume, can be clearly interpreted on the VP/VS data. Geobodies of low VP/VS are interpreted to represent the Ty sand. Above the OOWC, these geobodies tie to the geobodies of water sweep in the 4D data. Derived from different data, the correlation of these connected volumes reduces uncertainty in both the 4D and Ty sand interpretations. The updip limit of the Ty sands is better defined by VP/ VS from inversion and by better imaging of the 2001 survey as a result of its reprocessing associated with the 4D program. Seismic modeling suggests that the chalk reflection amplitude dims with the presence of the Ty sand (Figure 8). The integration of the inversion data and the 3D interpretation along with well control resulted in an elevated pinch out compared to the previously modeled Ty sand limit (Figure 5). Resource assessment Interpretation of the 3D and 4D seismic data resulted in a significant improvement in our understanding of the Ty

reservoir. Reprocessing of the 2001 baseline survey provided better imaging of the top and base reservoir and a more consistent regional depth conversion. In addition, VP/VS from simultaneous elastic inversion constrained sand presence. The 4D data lifted and expanded the flank of the reservoir based on the imaged water movement and provided seismic velocity information away from well control. The interpretations were validated by matching the swept volume obtained by the 4D seismic data with the produced volume. Based on the results from the 2006 4D monitor survey, a 2008 assessment led to a 40% increase in the resource base for the Ty reservoir. Of that increase, 60% was applied to proved and probable reserves based on the updated volumetric assess­ ment, continued strong reservoir performance, and planned infill drill wells. The 2009 4D monitor survey confirmed the locations of two planned Ty infill wells. The remaining 40% of the increase in the resource base was applied to the contingent category based on additional infill drilling potential and the possible development of attic oil and thin Ty sands. Conclusions Time-lapse seismic data have long been recognized as a tool used to identify areas of bypassed and undrained oil, improve the existing geologic model, and enable more cost-effective field operations. For the Ty reservoir in the Ringhorne West Field, 4D data help explain the timing of water breakthrough and improve our understanding of production history. The 2006 monitor survey identified several infill opportunities that were confirmed with a second monitor survey acquired in 2009. That survey reveals additional structural and stratigraphic details of the reservoir. In this paper, we show that 4D seismic data, combined with improved 3D imaging and elastic inversion, can also provide valuable constraints on resource evaluation. The 4D
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data help delineate the position of the OOWC, providing control on time-to-depth away from wells, and improve the interpretation of top reservoir. The result was a 40% increase in the resource base for the Ty reservoir.
Gouveia, W. P., D. H. Johnston, and A. Solberg, 2004, Remarks on the estimation of time-lapse elastic properties: The case for the Jotun field, Norway: 74th Annual International Meeting, SEG, Expanded Abstracts, 2212–2215; doi: http://dx.doi. org/10.1190/1.1839692. Johnston, D. H., U. Tiwari, M. B. Helgerud, and B. P. Laugier, 2010, New opportunities from 4D seismic and lithology prediction at Ringhorne Field, Norwegian North Sea: 80th Annual International Meeting, SEG, Expanded Abstracts, 4160–4164, doi: http://dx.doi.org/10.1190/1.3513732. Sarkar, S., W. P. Gouveia, and D. H. Johnston, 2003, On the inversion of time-lapse data: 73rd Annual International Meeting, SEG, Expanded Abstracts, 1489–1492, doi: http://dx.doi. org/10.1190/1.1817575.

References

Helgerud, and Peter Homonko. PGS acquired the 2006 monitor survey and CGGVeritas was responsible for processing. Exxon Mobil Corporation has numerous subsidiaries, many with names that include ExxonMobil, Exxon, Esso, and Mobil. For convenience and simplicity in this paper, the parent company and its subsidiaries may be referenced separately or collectively as "ExxonMobil." Nothing in this presentation is intended to override the corporate separateness of these separate legal entities. Working relationships discussed in this presentation do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship. Corresponding author: [email protected]

Acknowledgments: We thank ExxonMobil Exploration and Production Norway A/S and ExxonMobil Production Company for permission to publish this paper. We also thank our many colleagues who participated in the acquisition, processing, and interpretation of the data, especially Upendra Tiwari, Adam Bucki, Mike

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

Seismic technology supporting reserves determinations: Gorgon Field, Australia
Raphic van der Weiden, Prasanta Nayak, and Peter Swinburn, Shell Technology Center Bangalore

S

eismic technologies have been used in the industry in a rather restricted fashion in the context of assessing proved reserves. So far, limited additional application of seismic technology is seen under the new principle-based SEC guidelines. The applications are primarily confined to the use of seismic as a “reliable technology” as defined by the SEC (Sidle and Lee, 2010). An example of wider use of seismic technology as part of an integrated and holistic analysis to establish reasonable certainty required for proved reserves bookings by Shell is presented as a case study in this paper. Gorgon Field is a huge lean gas-condensate accumulation some 65 km to the west of Barrow Island, off the coast of Western Australia in water depths of 200–300 m (Figure 1a). The reservoir system consists of Triassic fluvial sandstones overlain unconformably by shales of the Early Cretaceous Barrow Group. The Triassic section divides naturally into a lower fine-grained sequence, the marine Locker Shale, and an upper, coarser grained sequence, the fluvio-deltaic Mungaroo Formation (Figure 1b). Mungaroo Formation is a sequence of stacked reservoir quality fluvio-deltaic sandstones and claystones deposited regressively over the Locker Shale during the Middle and Late Triassic. The Mungaroo exceeds 3000 m in thickness; approximately 2000 m of Mungaroo has been penetrated at Gorgon without reaching the base. It covers a vast area extending from onshore to several hundred kilometers offshore, and laterally over the entire North-

west Shelf. The Triassic stratigraphy is made up of a series of stacked fluvial sequences dominated by the processes of incision and aggradation (Radovich and Oliveros, 1998). These fluvial channel sands constitute the main reservoir sequence. Structurally, the Gorgon horst is oriented NE-SW and is bounded by steep faults, to the east with more than 1500 m of throw and to the west with more than 500 m of throw. Intrahorst faults have less than 100 m of throw, and subdivide Gorgon Field into several fault blocks (Figure 2). The field is covered by multiple 3D seismic surveys. The latest survey was acquired in 2006 with 6-km cable length, a CMP spacing of 25 × 18.75 m, and 80-fold coverage. The reservoir sands are acoustically soft and exhibit a class III AVO response. PSDM data as well as industry standard seismic inversion data were extensively used for supporting the proved reserves estimation. The field is planned to begin operations in 2014 (Parshall, 2010) and is operated by Chevron with 47.333% share. Shell and Exxon Mobil each hold 25%. The other joint venture partners are Osaka Gas (1.25%), Tokyo Gas (1%), and Chubu Electric Power (0.417%). The seismic data were used along with other geosciences and engineering data for arriving at a reasonably certain estimate of proved volumes of the Gorgon field. The strategy for using the seismic data is described in the following sections.

Figure 1. (a) Location of Gorgon Field. (b) Regional stratigraphic column of Carnarvon Basin. The Triassic Mungaroo Formation is mainly fluvial channel sands and is the primary reservoir for the Gorgon gas accumulation.
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Define with reasonable certainty the “reservoir tank” upon integrating seismic, well logs and wireline pressure data Under this heading, the first step was to establish the well-toseismic ties for each reservoir, resulting in unambiguously linking the top and base of reservoir in the Gorgon wells to the seismic events. Well-to-seismic ties (Figure 3) indicated one-toone correspondence of top and base of the major sands between synthetic and actual seismic with high cross-correlation factors. Six out of eight wells show cross-correlation factors greater than 0.65, while the remaining two older wells have poor log quality resulting in well ties of poorer quality. The second step was to establish confidence in the quality of the seismic data sets. The data have 80-fold coverage and the quality of the 3D seismic data is good with a bandwidth of 8–42 Hz at –6 dB cutoff. The operator provided prestack time-migrated and depth-migrated data as well as industry standard seismic inversion products (i.e., full and band-limited acoustic impedance volumes). There is good continuity of reflection strength and character (Figure 4) between wells and across faults in the full stack (5–40°) as well as in all substacks with near (5–20°), mid (18–32°), and far (30–40°) demonstrating the consistency of the seismic data. All seismic data show moderate-to-large fluid effects. Gas sands as well as low-saturation gas sands are soft and the sand top is displayed as a (positive) peak and the base is displayed as a (negative) trough in reflectivity seismic data. Seismic polarity is variable for brine sands. The third step was the mapping of the reservoirs and their extent away from well penetrations using seismic data, either to the limit of the seismic reflectivity of the sands, or up to major boundaries such as faults. As shown in Figure 5, the tuning thickness in the zone of interest is 28 m±4 m (~15 ms). The tuning model predicts that below a thickness of 5 m, the amplitude is reduced significantly and the reservoir becomes unmappable.

Below tuning, there is uncertainty in the reservoir thickness between the wells, therefore a number of different thickness realizations were created for each major sand. These were created by: • fitting a functional relationship between reservoir thickness encountered in wells and seismic amplitude, or • a functional relationship between reservoir thickness encountered in wells and two-way time interval as mapped in seismic, or • using seismic amplitude or two-way time intervals as a trend map in the extrapolation of reservoir thickness away from the wells. This resulted in up to 15 thickness maps per reservoir in which the highest and lowest in the range of thickness realizations were taken as the high and low case, respectively. A realization toward the middle of the thickness range was taken as the mid case. Limiting the reservoir to the edge of amplitude further increased the confidence in the volumetric estimate as in the proved case the dimming of the seismic response has been taken as that of zero reservoir thickness. Thin (<5 m thickness) sands may in reality continue beyond the modeled extent. Establish the “internal continuity of the reservoirs” with reasonable certainty To this end, the first step was to use the seismic interpretation and mapping to identify potentially offsetting faults (Figure 2). This is to ensure that reserves are not assigned to adjacent reservoirs isolated by major, potentially sealing faults. These mapped faults were analyzed using the reservoir juxtaposition plots and pressure data to establish whether there is communication across the faults or not.

Figure 2. (a) Sand-6 top depth map. (b) WE seismic section (TWT, full stack 90° phase shift) through Gorgon Field. In this 90° phase-shifted seismic data, the gas sand top and base are displayed as zero crossing and the (negative, red) event represents the gas-bearing sands.
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Figure 3. Representative well-to-seismic tie. The gas-bearing sand is soft. The sand top is displayed as a (positive) peak and the sand base is displayed as a (negative) trough in the reflection seismic data. There is one-to-one correspondence between the well synthetic and the surface seismic, demonstrating the unambiguous nature of sand identification in the seismic data.

There are several en-echelon faults in Gorgon Field which are disconnected with ramps in between providing pathways for communication between individual fault segments. One such example is the ramp between block-4 and block-5 (Figure 6). The seismic section along this ramp indicates continuity of reservoirs; thereby, the GWC of downthrown block-4 is used as GWC for upthrown block-5, which increased the proved reserves for block-5. Faults have been mapped to a detectable throw of 10–15 m. Faults with throw less than 10 m could exist, but are seismically unmappable (Figure 6). The subseismic faults could pose an uncertainty as they could affect the flow potential of the sands by reducing the transmissibility. However, the major reservoirs in Gorgon range from 20 to 70 m in thickness. It is highly unlikely that the throw of even the largest of the subseismic faults (<10 m) will completely offset the reservoirs. The second step was to extend the reservoirs away from wells over long distances within the field, using seismic interpretation techniques including amplitude extractions (Figure 7) and geobody extractions. The robustness of the correlations is further demonstrated by the ability to tie seismic events to these reservoirs and the ability to track these seismic events between wells (Figure 8). This provided direct insight into, and support of the presence and continuity of reservoir (Pichon et al., 2011) in any one zone, as well as the overall geologic trends, such as width and direction of the channel belts. The last step in establishing with reasonable certainty the “internal continuity of the reservoirs” was to look for seismic analogs in nearby fields with Triassic channel sands as primary reservoirs. In case of the nearby Achilles Field (Figure 9), highamplitude channel features are mapped at several levels and the
1052 The Leading Edge September 2012

Figure 4. NS seismic sections through full-, near-, mid-, and farstack seismic data show event continuity. The sand top is displayed as a (positive, blue) peak and the sand base is displayed as a (negative, red) trough in the seismic data.

well penetrated sands in those levels. The wells have not penetrated any sand where the high-amplitude channel feature is not mapped in seismic. Another example is Clio Field (Figure 9) in which the wells have penetrated sands where seismic data show high-amplitude channel features. There is a historic track record of sand predictability in 40 out of 40 exploration and appraisal wells.

Geophysics in reserves estim ation

Geophysics in reserves estim ation

Figure 5. Wedge model predicting the tuning thickness and the detectable limit. The wedge model is generated using the gas sand and the bounding shale acoustic properties and the wavelet derived from seismic data. The red seismic loop (positive) represents the gas sand top, while the blue seismic loop (negative) represents the gas sand base. Once the sand thickness goes below 5 m, the amplitude reduces significantly, making the sands unmappable. This reduction in amplitude helps in defining the edge of the seismically visible extent of the reservoir.

Figure 6. Major and minor faults are mapped using seismic attributes and traverses. Faults have been mapped to a detectable throw of 10–15 m. The semblance map shown here indicates the connectivity between block-4 and block-5 through the ramp. The seismic section through the ramp indicates continuity of sands between block-4 and block-5. In these 90° phase-shifted data, the gas sand top and base are displayed as zero crossing and the (negative, red) event represents the gas-bearing sands.

Establish the “reservoir properties and their continuity” with reasonable certainty For this purpose, the first step was to define the indicator which separates the sands from shales. All correlated and mapped reservoirs have lower acoustic impedances than their bounding shales. Figure 10 shows the well-derived acoustic impedance for the sands as well as the shale acoustic impedance line as function of depth. The shallow sands have a wider range but a larger contrast while the deeper sands have a narrower range and smaller contrast, when compared with the shale impedance line.
1054 The Leading Edge September 2012

At depth, the acoustic impedance contrasts between reservoir and nonreservoir decreases because of compaction of the sands, but still a measurable separation remains. The contrast of 500 acoustic impedance units is considered as a robust indicator of the sand line. The sensitivity at higher and lower thresholds of acoustic impedance was analyzed to confirm that the mapped sand shapes were robust and reliable. The second step was to use the learnings from the previous step which demonstrated that impedance contrast between sand and shale in Gorgon field is a good separator of lithology,

Geophysics in reserves estim ation

generating the need to look into seismic inversion (Swinburn et al., 2011). The operator performed an industry standard (Fugro-Jason) single-stack constrained sparse-spike seismic inversion (CSSI) on the PSDM 3D seismic. Band-limited and full-band acoustic impedance volumes were available. The derived wavelets (Figure 11a) were consistent in their bandwidth

and phase behavior. The zero-phase behavior is clearly visible for all wavelets (shown as 180° rotated). The good acoustic impedance well matches (example shown in Figure 11b) show that the acoustic impedance data can be used to predict sands from seismic away from well control. At a contrast of zero acoustic impedance units (shale line, see Figure 10), additional seismic bodies have channel belt geometries. Only below a contrast of –800 acoustic impedance units, does the main seismic body start to break up (Figure 12). The mapped sand body shapes extend at least over the proved areas. Finally, the relationship between acoustic impedance and reservoir properties was investigated. The linear relationship between total porosity in the gas sands versus full-band acoustic impedance (Figure 13a) in the wells was used to derive porosity for the reservoir sands. Figure 13b shows reservoir sands with an acoustic-impedance-derived porosity cutoff of 7% (equivalent to a permeability of 0.01 mD). The reservoirs are noticeably continuous and contain only few isolated areas (in black) with predicted porosities less than 7%. These porosity

Figure 7. Relationship of far-stack (90° phase shift) seismic-derived amplitude of Sand-3 to reservoir thickness and continuity. The wells penetrating the high-amplitude area have encountered Sand-3 while the well in the low-amplitude area is devoid of reservoir.

Figure 8. Extending geologic correlations away from the wells using the full-stack seismic data. In these 90° phase-shifted data, the gas sand top and base are displayed as zero crossing and the (negative, red) event represents the gas-bearing sands.

Figure 9. Reservoir predicted from seismic versus actual well results. (a) Achilles Field and (b) Clio Field. In Achilles Field, the high-amplitude channel features are mapped in the L1 and L3 levels and the well has penetrated sands in these levels. The well has not penetrated any sand in the L2 level and the seismic shows low amplitude. In Clio Field, wells have penetrated sands in the L4 level which are mapped as high-amplitude channel features in seismic.
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maps were qualitatively used to support reservoir continuity and extent. The above analysis and interpretation of the seismic and acoustic impedance data were integrated with well data to create an expectation case static model. A full-field uncertainty analysis was carried out to identify the key static parameters

such as reservoir thickness, time-depth conversion, porosity, gas saturation, etc. To generate a high-confidence case which formed the basis for the proved reserves, these uncertainties were combined with the expectation case static model in a probabilistic way. Dynamic response of the field was established to demonstrate economic producibility. Both reflection and inversion seismic data were exhaustively used to demonstrate the continuity of reservoirs beyond the radius of investigation as seen by the well tests. As a consequence, a larger proved area and proved reserves determination could be established. Conclusions Seismic interpretation and quantitative interpretation in combination with integrated reservoir modeling was extensively used by Shell for reserves booking purposes under the principle-based SEC rules. The new rules are not prescriptive and require attention to details to quantify and clearly demonstrate the reliability of the data. In this integrated analysis underpinning the proved reserves determinations under the principle-based SEC rules the geophysicist plays an important role.
References
Parshall, J., 2010, Gorgon to drive Australian LNG Asia-export surge: Journal of Petroleum Technology, November, 32–35. Pichon, P.-L., S. Delahaye, G. Fabre, and P. Desegaulx, 2011, DHI confidence assessment for field evaluation: an integrated geosciences necessity: 81st Annual International Meeting, SEG, Expanded Abstracts, 1140–1144, http://dx.doi.org/10.1190/1.3627404. Radovich, B. J. and R. B. Oliveros, 1998, 3D sequence interpretation of seismic instantaneous attributes from the Gorgon Field: The Leading Edge, 17, no. 9, 1286–1293, http://dx.doi.org/10.1190/1.1438125. Sidle, R. and W. Lee, 2010, Qualifying seismic as a “reliable technology”—an example of downdip water contact location: SPE paper 134237.

Figure 10. Well-derived impedance as a function of depth. The shallow sands have a wider impedance range and a larger contrast while the deeper sands have a narrower range and smaller contrast, when compared with the shale impedance line. At depth, the acoustic impedance contrasts between reservoir and nonreservoir decrease because of compaction of the sands, but still a measurable separation remains.

Figure 11. (a) Wavelets derived over sufficiently large windows show consistency in phase and amplitude. (b) QC panel showing good match between well-derived and seismic-inversion-derived acoustic impedance. Gas sands are soft and the input reflectivity seismic data display the sand top as a (positive, blue) peak and the base as a (negative, red) trough. The software used for this inversion study internally uses reversed polarity for wavelet extraction and matching.
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Geophysics in reserves estim ation

Figure 12. Sand-3 band-limited acoustic impedance showing continuity at various cutoffs. Sand-3 (yellow) is continuous and extends over the proved area. Only below a contrast of –800 acoustic impedance units does the main seismic body start to break up.

Figure 13. (a) Acoustic impedance-porosity relationship for gas sands. (b) Sands with acoustic-impedance-derived porosity higher than 7% in the proved areas.
Swinburn, P., P. Nayak, and R. van der Weiden, 2011, Use of seismic technology in support of reserves booking, Gorgon field, Australia: 81st Annual International Meeting, SEG, Expanded Abstracts, 1129– 1133, doi: http://dx.doi.org/10.1190/1.3627401.

thanks to Chevron Australia Pty. Ltd. for providing technical products such as the seismic data sets. The work and views expressed in this paper are those of the authors and do not necessarily reflect the views of the joint venture companies. Corresponding author: [email protected]

Acknowledgments: The authors thank Shell Development Australia Pty. Ltd., Shell Technology Centre Bangalore, Chevron Australia Pty. Ltd., ExxonMobil Australia Pty. Ltd., Osaka Gas, Chubu Electric Power, and Tokyo Gas for permission to publish this paper. Special
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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

DHI support for resources evaluation: Confidence assessment examples
Pierre-Louis Pichon, Sabine Delahaye, Greg Fabre, and Pascal Desegaulx, Total SA

ithin a field evaluation, seismic events, such as flat spots, amplitude anomalies, or other seismic-derived attributes are often used in the interpretation process. When these seismic events are demonstrated to be robust enough, consistent with well data and the geologic scenario, they can be qualified as direct hydrocarbon indicators (DHIs) and used as key elements to assess fluid contacts, reservoir extension, and compartmentalization. This paper presents three examples of DHI integration into resources evaluation, thanks to a confidence assessment methodology necessary to evaluate the DHI robustness and certainty. The DHI confidence assessment includes two complementary evaluations: • The first one assesses the seismic and petrophysical data quality, and their ability to properly represent the seismic response for the known (or expected) reservoir and fluid characteristics. It analyzes the DHI detectability and its type according to different geologic contexts (burial, lithology variations, etc. ); • The second one evaluates the consistency of the geophysical information with the geologic and dynamic knowledge of the field including their uncertainties. This “cross-view” is fundamental in the process, as the consistency between independent information ranging from exploration/delineation well results (reservoir properties, fluid saturation, pressures, DST, etc.) to field production behavior is the key to confidence. Three examples extracted from our analog database will be reviewed to illustrate in different application contexts (contact definition, panel connectivity evaluation) the DHI input for the field evaluation through the confidence assessment methodology. They illustrate the necessity of an integrated approach which goes beyond the DHI technical aspects to ensure geologic scenarios consistency associated to certainty criteria. This DHI assessment as well as the quality control it infers can represent a significant aspect of the geophysical input to the field evaluation which also has to address other characteristics of

W

the field such as its structural shape, the accumulation perimeter definition, the reservoir geometry and quality, and other elements impacting the static and dynamic evaluations, along with the associated reserves-resources. A DHI evaluation workflow is used in Total to assess the seismic and petrophysical data quality and evaluate the consistency of all available data. This approach goes well beyond geophysics and integrates all available data to evaluate the interpretation consistency and associated uncertainties. In the first part of this article, the workflow will be described with the focus on the main steps and elements of both DHI quality and overall field consistency evaluations. It will then be illustrated through three practical examples. DHI confidence assessment workflow It is important to first define a confidence assessment workflow to ensure a comprehensive and systematic approach. It provides the seismic interpreter and the geosciences evaluation team with guidelines to ensure reliable work (and a thorough review of the data that emphasizes interpretation consistency and avoiding pitfalls). This workflow can be subdivided into two main assessment steps: DHI quality and overall hydrocarbon accumulation consistency. DHI quality assessment Using a DHI necessitates first evaluating the seismic data quality in terms of reliability, adequacy to reservoir characteristics, and limitations of use. The acquisition and processing parameters must be reviewed according to objectives. Extensive data quality checks are performed to evaluate the related uncertainty and recommend improvements when necessary. This step is fundamental in order to get a clear view of the advantages and weaknesses of the seismic data which will be interpreted. Petro-elastic data also must be fully analyzed starting from the quality control and accurate editing of the logs, the reliability of petro-elastic models, and of multiscenario fluid substitutions are evaluated. Then the well-to-seismic calibration (including prestack) is analyzed in detail; input data and methods used must be fully documented. This step may impact the processing scheme itself if performed early in the sequence. The matching quality indicates how reliably the seismic information can be interpreted through a smaller scale petro-elastic behavior. A DHI corresponds to a specific attribute response within the seismic data sets. But frequently, for its analysis, this information
Editor's note: This article was expanded by the authors from "DHI confidence assessment for field evaluation: An integrated geosciences necessity," SEG Expanded Abstracts 30, 1140 (2011), doi: http://dx.doi. org/10.1190/1.3627404.

Figure 1. Example 1. Contacts definition from seismic cross-section and attribute map.
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Figure 2. Example 1. Contacts definition from attribute maps and pressure plots.

is extracted from 3D cubes using interpreted surfaces. The adequacy of such extraction also needs to be assessed. As a result, the DHI quality is controlled in terms of characterization, representativeness, reliability and uncertainties, in both geophysical and petrophysical domains. Overall accumulation consistency assessment The second step aims at evaluating the consistency of the DHI information and related uncertainties with other independent geoscientific knowledge. First, the DHI must be consistent with other aspects of seismic interpretation, from structural to sedimentary; a flat spot or

amplitude dimming/brightening expected to correspond to a fluid contact should usually be at a constant depth for a given panel, within the time-to-depth conversion uncertainty. Its extension should be consistent with the reservoir limits and top depth map to contact. Additionally, it must be consistent with the trapping history (underfilled structure, spill point, stratigraphic trapping, sealing faults, etc.). It also must be challenged with other geologic data from wells, sedimentary models, and regional knowledge such as reservoir facies heterogeneity, spatial extension and thickness, compaction trends, reservoir continuity across faults, diagenesis, etc. Pressure and fluid information have to be considered: depth
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In the upper interval of the reservoir, well GF-1 encountered a gas-oil contact, which shows an excellent conformance to the structure (Figure 1). However, the oil-bearing part of the reservoir is defined only by an “oil down to” within both upper and lower reservoir intervals. The objective is to estimate the depth of the water-oil contact using available information and to define the associated degree of confidence. The seismic quality is good; it is a high-resolution marine seismic, prestack-migrated in time with a fourth-order velocity correction applied (ensuring long-offset reliability), and exempt of imaging issues at the objective. It allows a detailed structural and sedimentary interpretation, with a resolution greater than reservoir thickness and a reliable time/depth conversion model calibrated at wells. A clear petro-elastic difference between oil and water sands is evident: • Fluid substitutions based on well logs allow the modeling of oil-bearing and water reservoir responses. The match with the actual seismic response (including GOC) is excellent. • AVO calibration from nearby wells gives confidence to fluid contact interpretation. A detailed interpretation of both upper and lower reservoirs shows that they are connected together, and the seismic attribute responses of those reservoirs are coherent with both the petroelastic modeling and the sedimentary/structural reservoir extension interpretation. Additionally, the conformance between the shutoff of the calibrated seismic attributes (substacks, AVO, inversion) and an iso-depth line is clearly seen (Figure 2), within both upper and lower reservoir extensions and consistent with the spill limits. The upper and lower reservoir connectivity as well as the DHI depth are confirmed by the GF-1 WFT pressure gradient, the interpretation of its oil-pressure trend, and intersection with the regional aquifer trend. The quality of the DHI response and consistency between independent information (pressure data, depth conversion, sedimentary interpretation, etc.) provides a high confidence to the WOC depth estimate. The corresponding hydrocarbon volumes may therefore qualify as reserves assuming all other criteria are met. Compartmentalization evaluation: Example 2 On the second example, well-1 drilled a turbiditic channel and encountered a stack of several oil-bearing sands. A WOC was recognized at the well and confirmed with pressure measurements. All sands are vertically connected. The seismic has good resolution with a high signal-to-noise ratio. The seismic-to-well calibration of the drilled channel and encountered WOC is good. Several nearby wells can be used to model the seismic responses for different fluids and net-to-gross ratios, thanks to a thorough petro-elastic study. Two panels, B and C on Figure 3, are fault-separated from the main one (A) which is drilled. Within the turbiditic channel extension, all panels show the same amplitude and a mix of sedimentary and fluid responses.

Figure 3. Example 2. Compartmentalization analyses on seismic channel random line and amplitude map.

of fluid contacts and related uncertainties, connectivity between reservoirs and compartments, fluid characteristics homogeneity, PSAT uncertainties versus GOC, seal retention capacity versus hydrocarbon column, etc. Production test or history matching results will also provide constraints on possible contacts and flowing limits. 4D seismic, when available, provide useful information on the definition of the flow units, compartmentalization, fluid movements, etc. By compiling all these independent analyses, a robust understanding can be achieved, with associated uncertainties. Alternative geological scenarios also must be investigated, and ranked in term of probability of occurrence. Thanks to this thorough analysis, the DHI can be integrated with the proper level of certainty to the global field evaluation. Contacts evaluation: Example 1 This first example corresponds to a clastic reservoir drilled by a unique well.
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Geophysics in reserves estim ation
An extensive structural and stratigraphic interpretation has been performed with detailed sedimentary features for geologic modeling, which demonstrates that the lower part of the upthrown C panel is connected with the upper part of the A downthrown panel. A clear juxtaposition of these sands appears on the amplitude and on the pseudo-vclay cross-sections (Figure 3). The absence of a visible WOC within the C panel is consistent with the geologic scenario, as the reservoir is expected to be fully oil-bearing. Nevertheless, to confirm that no logical alternative scenario can be envisaged, a massive modeling of reservoir and fluid response has been performed. The results show that the seismic attributes seen in the C panel are not compatible with either gas- or water-bearing reservoirs with different lithologies/ thicknesses or with a calibrated gas response encountered in a nearby fairway. The reliability, within the specific geologic context, of the DHI information has been fully assessed, providing high certainty for the B and C panels to be oil-bearing. A high confidence in the connectivity between those panels is provided by the reservoir continuity assessment from the seismic sedimentary and structural interpretations. The consistency of those independent studies provides a high degree of confidence regarding the presence of oil in the B and C panels, and corresponding resources evaluation. Contacts-compartmentalization evaluation: Example 3 This case also involves a turbiditic reservoir corresponding to a distal lobe eroded by a channel. A well (well-2) recognized the lobe and discovered rich gas. Only a GDT can be defined from the well as no contact was encountered. The anisotropic PSDM was designed to achieve both structural and stratigraphic objectives. Its quality is excellent in terms of resolution and SNR. A strong amplitude attenuation (and other attributes, reliably calibrated in a similar manner to example 1) shows a good conformance to the structural depth map within the reservoir extension (Figure 4). Nevertheless in this specific case, the seismic presents a clearly different response between hydrocarbon and water, but oil and gas fluids cannot be discriminated. Thus the amplitude attenuation relates to a hydrocarbon-water contact. From the pressure studies, and using assumptions from nearby wells and uncertainties about the aquifer pressure, a contact depth range has been estimated. By comparing the range of uncertainty on contact depth from independent estimations of the DHI, pressure trend analysis, known GDT (gas down to) at well and spill depth, it turns out that the seismic information provides the most precise estimation of the contact depth, while being consistent with other estimates (Figure 5). But the main issue to tackle in this example is the connection between the three panels: L, S, and G (Figure 4). On all three panels, the seismic response (bright amplitude) is consistent and interpreted as related to the presence of hydrocarbons. A full reservoir connectivity assessment has been performed from detailed structural and sedimentary interpretation: reservoir bodies communication analysis, flexural versus brittle fault analysis, Allen diagram, cross sections, etc. This analysis has shown that between panels S and L, the

Figure 4. Example 3. Compartmentalization analyses on amplitude map and seismic random lines.

All along the main compartment (A), a clear seismic flat spot, which corresponds to the WOC calibrated by well-1, is identified on angle stacks, AVO, or inverted seismic data. A similar feature is interpreted at the base of the B compartment. But for the C panel in which all the reservoir sands are above the WOC encountered in the A panel, no indication of contact can be seen on seismic. The fault throw between A and C is smaller than the channel sedimentary unit thickness, but greater than the average thickness of individual, interconnected sands.
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Figure 5. Example 3. Uncertainties for contact.

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throw of the F1 fault is smaller than the thickness of the drilled reservoir and sands are clearly connected. The F2 fault connects both parts of the S panel through a relay zone. However, a sand juxtaposition between the S and G panels through the F3 fault cannot be ensured with reasonable certainty, although it remains a most likely case, with seismic amplitude and DHI characterization similar to the S panel. Accordingly, the L and S panels are considered with high confidence as connected and sharing the same fluid(s) and HWC contact. For the G panel, the base case considers the L and S panels as connected, with a consistent DHI, as the reservoir connectivity is considered likely; but this connection cannot be considered as proven from the available data. Conclusions DHIs can represent significant information to assess fluids, contacts, and compartmentalization in a drilled structure. Nevertheless it necessitates a robust confidence assessment to enable its integration with reserves and resources evaluation, within defined certainty criteria. The DHI confidence assessment is performed through two main aspects : • its reliability and technical applicability in the specific context • its consistency with the geologic scenario and independent fluid/contact estimates In addition, this assessment process provides the seismic interpreter with good guidelines to ensure reliable work (a thorough review of the data, emphasizing their consistency, avoiding pitfalls and checking consistency with nongeophysical data). The three examples in this paper show the usefulness of such an approach to integrate reliable DHI information to the reserves/resources evaluation with the appropriate level of certainty. In certain circumstances, this integration of seismic information may significantly reduce the uncertainties.
Acknowledgments: The authors thank the contributors of the study examples and the management of Total for permission to publish this work. Corresponding author: [email protected]

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

Relating seismic interpretation to reserve/resource calculations: Insights from a DHI consortium
Rocky Roden and Mike Forrest, Rose and Associates Roger Holeywell, Marathon Oil Company

n the process of quantifying resources/reserves, geoscientists attempt to employ all the available pertinent information to produce the most accurate results. The presence of direct hydrocarbon indicators (DHI) on seismic data can have a significant impact on the reserve/resource calculations not only for volumes, but also uncertainty levels. In 2001 a consortium of oil companies was organized in an attempt to understand seismic amplitude anomalies interpreted as DHIs and their impact on prospect risking and resource calculations (Roden et al., 2005; Forrest et al., 2010). The geologic setting, seismic and rock physics data quality, DHI characteristics, and calibration of drilling results are all incorporated into a database in a consistent and systematic process. From this process, the evaluation of 217 prospects and associated well results has enabled an in-depth understanding of the relevant key aspects of seismic amplitude anomalies and how they relate to drilling results. DHI Consortium background In the DHI Consortium, 41 oil companies have contributed to develop a seismic amplitude analysis process that systematically provides probability of geologic success (Pg) values and associated implications for resource evaluations (Figure 1). The goals of the DHI consortium over the past 11 years have been and continue to be the following:
1) Gain a better understanding of how seismic amplitudes

I

4) Use the prospect database to improve the predictability of 5) Improve the DHI work analysis process to help risk seis-

Pg and range of uncertainty of hydrocarbon volumes.

mic amplitudes and provide an educational tool for interpreters. 6) Discussion and review of pertinent technologies for amplitude interpretation. These companies have supplied 217 prospects from around the world (Figure 2) with approximately an equal number of successes, dry holes, and objective reservoirs ranging in age from Triassic to Pleistocene. The prospects range in size from less than 100 acres to greater than 10,000 acres and depths from 2000 to more than 20,000 ft. This database is predominantly made up of exploratory wells with only 14% coming from “development” or “extension to known reservoir” wells (Figure 3). The closure types include structural (59%), stratigraphic (26%), and combination structural/ stratigraphic (15%) traps. Critical in the evaluation of DHI characteristics is an understanding of the geologic setting. Because expected DHI responses vary depending on the geologic setting, the prospects in the database are categorized by AVO classes 1–4 as displayed in Figure 4 (Rutherford and Williams, 1989; Castagna et al., 1998). However, 76% of the prospects are class 3 and 22% are class 2, with only a few from class 1 and 4 settings. A general description of the input information and quantified output for this database are described in Table 1. There are several ways the most important DHI characteristics can be determined from the well results. The following list is a result of when a DHI characteristic was rated to be a 4 or 5 on a scale of 1–5, with 1 being the worst and 5 the best. These results were then correlated with whether the wells were successful or dry holes. This approach eliminates any scaling or weighting effects and compares only whether the DHI characteristic is strongly correlated with the actual
Input Geologic context Quantified output Composite data quality score

impact prospect risking (i.e., predrill chance of geological success or Pg). 2) Objectively characterize DHI observations using documented occurrences of recoverable hydrocarbons in the subsurface via prospect reviews and risk analysis discussions. 3) Archive a statistically significant library of drilled prospect results.

Initial Pg (product of geologic DHI index (Pg specifically aschance factors independent of sociated with DHI anomaly) amplitude as a DHI) Seismic data quality Rock physics data quality Final Pg Calibration of final Pg

DHI characteristics observations Volume calculation impact Figure 1. DHI interpretation and workflow concept.
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Table 1. General input and quantified output from DHI analysis process. Note that the input parameters are not necessarily linked directly to the corresponding quantified output row.

Geophysics in reserves estim ation

Figure 2. Locations around the world of the 217 wells in DHI Consortium database.

postdrill well results. The top five DHI characteristics have been determined for both class 2 and 3 AVO categories, which relate to different geologic settings, and for which there are a sufficient number of example prospects to be meaningful. A class 3 AVO setting relates to an unconsolidated gas sand with a gross interval velocity usually less than 8500 ft/s (2650 m/s). These reservoirs typically are low-impedance sands encased in relatively higher-impedance shales. Gas sands in this setting exhibit high amplitudes on the stack section as well as all offsets and angles. The seismic response from the top of these reservoirs produces high amplitudes with respect to background and increase (at times only slightly) with increasing offset/angle (Figure 4). Top five class 3 DHI characteristics
1) Amplitude downdip conformance (fit to closure) on stacked

or far offset seismic data. This is the top-rated DHI characteristic in the database and describes how well the seismic amplitudes of the anomaly conform to the downdip structural contours of the reservoir (Figure 5). Key issues are whether the structural contours are in time or depth, potential velocity variations that can affect imaging, accuracy of how the amplitudes were picked (top, bottom, or window) at the anomaly, and consideration of any stratigraphic variations. It is important to remember that this characteristic relates only to the downdip edge of the amplitude anomaly and how it compares to the interpreted structure. It does not relate to an amplitude’s conformance on the flanks of stratigraphic traps. This lateral conformance characteristic in stratigraphic traps depends on the geologic model and the strength of the independent evidence that supports that model.

Figure 3. Prospect types of wells in the DHI Consortium database.
2) Phase or character change at downdip edge of the anomaly.

This DHI characteristic relates to a change in character such as phase or frequency at the downdip edge of the anomaly. As the model in Figure 6 displays, the transition from the hydrocarbon leg to the water leg in a sand can produce a seismic response indicative of the edge of a reservoir. 3) Amplitude consistency in mapped target area. The internal consistency of the seismic amplitude in a reservoir was found to be a significant DHI characteristic. This relates to the uniformity of the amplitude response within the mapped target area as interpreted from the stacked seismic data (Figure 7). When evaluating this characteristic, conSeptember 2012 The Leading Edge 1067

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sideration should be given to possible faulting and stratigraphic changes that may modify internal consistency. 4) Flat spots. This characteristic represents the seismic response at a hydrocarbon contact that presumably is relatively flat. This contact can be at the gas/oil contact, oil/ water contact, or gas/water contact. The gross reservoir thickness of the hydrocarbon unit must be greater than the tuning thickness (vertical resolution) to image a flat spot. Extreme care must be taken because the base or edge of channels, low-angle faults, diagenetic boundaries, or even processing artifacts are often misinterpreted as flat spots. Low-saturation gas in a reservoir can also produce a flat spot and this possibility is usually assessed by regional geologic studies.

5) Excluding possible stacked pays, the AVO response is anoma-

lous compared to events above and below. On prestack data, usually gathers, this characteristic refers to whether there are high-amplitude events above and below the targeted anomaly that look similar (Figure 8). The reasoning is that the anomaly is relatively unique suggesting a hydrocarbon bearing reservoir.

A class 2 setting contains gas sands more consolidated than class 3 sands with gross interval velocities usually between 8500 ft/s and 12,000 ft/s (2650 m/s and 3650 m/s). The acoustic impedances of these gas sands and encasing shales are about equal. The intercept or near-offset amplitude can vary from a weak positive to a weak negative. The AVO effect can be strongly more negative with offset (large gradient) in these settings (Figure 4). Top five class 2 DHI characteristics 1) Amplitude downdip conformance (fit to closure) on far-offset seismic data. The principles behind this characteristic are the same as the top characteristic for class 3 settings, but for class 2 prospects this is usually evaluated on the far offset seismic data. 2) Consistency in mapped target area (typically on gathers, faroffset/angle stacks, or windowed attributes). Internal consistency of the seismic amplitude identified on far-offset data was found to be an important DHI characteristic for class 2 rocks just as it is for class 3 rocks. 3) AVO observations using gathers, far-offset/angle stacks, or windowed attributes. This characteristic relates to an interpreter’s confidence that the AVO response is proper for a class 2 setting. In other words, the near-offset is low in amplitude (small peak or trough) and the amplitude increases in negative amplitude with offset (Figure 4). Noisy gathers, incorrect NMO corrections, multiples, insufficient offset during acquisition, and processing artifacts often complicate evaluation of this characteristic.

Figure 4. (top) AVO classes based on amplitude change with offset from the top of gas sands (Rutherford and Williams, 1989; Castagna et al., 1998). (bottom) Angle gathers, zero-offset, and stack responses for typical class 2 and 3 AVO classes.

Figure 5. Examples of amplitude conformance to the downdip structural contours from grade 1 (worst) to grade 5 (best).
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Geophysics in reserves estim ation

4) The AVO event is anomalous compared to the same event

outside the closure. This characteristic describes whether the class 2 AVO response is unique compared to the correlative event outside the closure. This characteristic is often interpreted from far-offset/angle stacks, as well as, intercept, gradient, intercept x-gradient, far-near, and (farnear) x-far displays. 5) Change in AVO compared to model (wet versus hydrocarbonfilled). Modeling of the AVO response, usually applying Gassman’s equation, typically involves substitution modeling of gas, oil, and water responses. A comparison of the in-situ and modeled responses to actual gathers provides confidence that hydrocarbons are present or not. Reasons for failure The consortium defines successful wells as geologic successes (i.e., wells with flowable hydrocarbons). However, in this database all successes were determined to also be commercially successful except for a few wells. These exceptions contained from 50 to more than 100 ft of gas, but were determined to be noneconomic because of their location in the world and lack of infrastructure. Wells containing only low-saturation gas are considered by the consortium to be dry holes even though they may represent successful predictions of acoustic response. Wet sands in the target interval accounted for 49% of the dry holes in the database. Nearly half of these wet sands were thick. In fact, these thick wet sands were greater than the predrill P10 estimates for reservoir thickness. Hard shale on top of a wet sand, blocky sands with higher than expected porosities, and tuning effects of wet sands all produced amplitude anomalies misinterpreted to be direct hydrocarbon effects. Low-saturation gas (LSG) accounted for 23% for the failures in the database. This well known phenomenon is caused by a small percentage of residual gas in a reservoir (5–10%) producing an acoustic effect similar to commercial saturation and is usually associated with a breach or break in reservoir seal. All but one of the LSG dry holes were in class 3 rocks,

with one in a class 2 setting. All but one of the LSG dry holes were in normally pressured sediment columns. Four of the LSG wells exhibited acoustic flat spots indicative of a paleo hydrocarbon contact. Low vertical effective stress caused by undercompaction accounted for several LSG results in deepwater settings, but was also found where a relatively young sediment column overlaid the amplitude anomaly. Even though not dry holes, several wells exhibited hydrocarbons, usually gas, overlying a LSG column in the sand. No reservoir present was responsible for 17% of the dry holes. Amplitudes misinterpreted to be hydrocarbons included low-density shale, ash, coal beds, top of hard overpressures, seismic processing artifacts, mud volcano, and diagenetic boundary. Tight reservoirs accounted for 11% of the dry holes in the database. Examples of these include oil-charged marl, gas-charged condensed section, low-impedance siltstone/ mudstone and low-permeability reservoir. Implications for resource calculations in exploration The presence of direct hydrocarbon indicators can have a significant impact on methods to compute prospective resources, especially in exploration settings. How do DHIs change area determination for resources? How do DHIs

Figure 6. Model of hydrocarbon and water legs of a reservoir and location of possible phase or frequency change at edge of fluid contact (courtesy Quantum Earth Corp).

Figure 7. Examples of amplitude consistency within a defined DHI anomaly from grade 1 (worst) to grade 5 (best).
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Figure 8. An unmuted CDP gather displaying an AVO anomaly that appears prominent compared to the events above and below (Roden et al., 2005).The near vertical lines represent offset angles (purple = 10°, red = 20°, green = 30°).

impact thickness determinations in resource computations? How does a seismic-amplitude (DHI) defined area and thickness relate to a geologically defined area and thickness? The presence of true direct hydrocarbon indicators indicates that all geologic chance factors (e.g., source, migration, seal, reservoir, and trap) are working to form a petroleum system; therefore, the critical issue is determining the confidence level that the seismic is truly displaying a direct hydrocarbon indicator because not all anomalous amplitude events are DHIs. For area determinations, one approach is to consider two separate distribution estimates. One distribution is based on geologic evidence, independent of the amplitude as a DHI, while the other distribution is based on the amplitude-defined area (Figure 9). Each distribution can be defined by a P10 (largest reasonable) and a P90 (smallest reasonable) value. The variance or P10/P90 ratio of the area is often large for the geologic distribution, but much smaller for the amplitude-defined distribution because the anomaly usually has a sharp cutoff. From statistics based on the DHI consortium results, a “DHI Index” has been calculated that indicates the likelihood that the interpreted amplitude

Figure 9. Illustration of DHI Index method to define and weight geologic and amplitude areas for resource calculations.
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Figure 10. DHI Index values color-coded by drilling outcomes. Note that above 20% almost all wells are successful. There are negative DHI Index values, usually associated with dry holes, where DHI characteristics should be present, but were absent.

Figure 11. Seismic inversion techniques typically applied from exploration to development.

anomaly is truly a direct hydrocarbon indicator. This “DHI Index” can be used as a relative weighting factor for the two distribution estimates. The histograms of Figure 10 show that, when the DHI
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Index is more than 20%, essentially all wells are successful. To get a DHI Index of 20% or higher requires having numerous positive DHI characteristics. Therefore, using this logic, prospects with a DHI Index of 20% or larger employ the DHI-defined area, whereas, a DHI Index of 0% or less employ the geologically defined area (Figure 9). A DHI Index of 0% or less indicates there is no DHI element at all in the area determination. When a prospect has a DHI Index between 0% and 20%, the area is defined by a simple linear weighting (e.g., a DHI Index of 10% would have a 50/50 weighting of geology and amplitude-defined areas) between the two distribution estimates. For thickness determinations, similar to the DHI Index approach for area, two thickness distributions can be estimated. One is defined geologically and one by the amplitude-defined thickness. The geologically defined distribution should be based on well control, sand studies, depositional trends, and usually involves constructing regional and local sand isopachs consistent with the depositional model in terms of reservoir shape and thickness. From the combination of sand isopachs, depositional environment determinations, models and stratigraphy studies, an estimate of the P10 and P90 average net pay thicknesses can be calculated. For the amplitude-defined thickness, the interpreter first needs to determine whether the anomaly is above tuning, below tuning, or both. Above tuning, the thickness is a func-

Geophysics in reserves estim ation

tion of the time separation between the top and bottom of the anomaly. Gross pay maps (isochrons) can be generated for the P10 and P90 cases including any geometric factors. An estimated net-to-gross and interval velocity are applied to determine the final thickness distributions. Below tuning, in the ideal situation, the composite amplitude from the top and bottom of the bed decreases linearly with thickness starting from tuning thickness. The amplitude should be calibrated with well control and stratigraphy studies. Below tuning, a starting point P10 could be the tuning thickness and a P90 could be the detection limit (approximately 1/30 wavelength of the dominant frequency). Appropriate interval velocities may need to be applied for thickness conversions. Below tuning, the composite amplitude usually incorporates any net-to-gross effects. Once a geologically defined thickness distribution and a seismic amplitude-defined thickness are determined, the DHI Index can be used to determine weighting of these two distributions. It can be more difficult to separate and differentiate between a geologically and amplitude-defined thickness as compared to interpreting distributions for area. In this situation, it may be more appropriate to combine geologic and amplitude thickness information and use one distribution without weighting to compute resources. Spectral decomposition has been found to be helpful in certain situations to help determine thickness trends. Employing the DHI Index method to determine area and

thickness for resource calculations is one approach that gives credit to both geologically and amplitude defined distributions. However, it is not uncommon in strong amplitudedriven plays to employ the amplitude-defined distributions only, especially for area. The logic here is that the presence of amplitudes is defining the reservoir and the geologic distributions do not apply. Care should be taken in that all amplitudes are not DHIs and not all geologic settings exhibit DHIs equally. In addition, even if there is evidence that the area can be narrowly estimated, there will often be significant remaining uncertainty in resource size because of the variance in average net pay thickness and recovery factor estimates. Implications for reserve calculations The U. S. Securities and Exchange Commission (SEC) defines “proved oil and gas reserves” in part as “those quantities of oil and gas, which, by analysis of geosciences and engineering data, can be estimated with reasonable certainty to be economically producible—from a given date forward, from known reservoirs, and under existing economic conditions, operating methods, and government regulations—prior to the time at which contracts providing the right to operate expire, unless evidence indicates that renewal is reasonably certain, regardless of whether deterministic or probabilistic methods are used for the estimation.” Unlike resource calculations in exploration, reserve calculations have had a well

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or wells drilled to confirm geologic conditions and to some degree seismic amplitude features. Therefore the interpretation of DHI features relates to the determination of “reliable technology” as defined by the SEC. As defined by the SEC, “reliable technology is a grouping of one or more technologies (including computational methods) that has been field tested and has been demonstrated to provide reasonably certain results with consistency and repeatability in the formation being evaluated or in an analogous formation.” Therefore for DHI characteristics to qualify as reliable technology requires the correlation of amplitude features with well control, reservoir engineering tests, known geologic trends and stratigraphy. Even with well control and substantial geologic control of thickness and area extents, care should be taken in incorporating DHI information from seismic data. Considerations for amplitude anomalies and reserve determinations include:
1) Strong amplitude is primarily at wedge where tuning oc2) Stratigraphic trap where conventional downdip and updip 3) Reservoir thins updip, bald structure 4) Velocity tilt can affect conformance to closure interpreta5) Faults affecting amplitudes for area determinations 6) Stratigraphic changes across field 7) Masking of amplitudes from bright spots above prospect 8) Amplitude effects from low-saturation gas downdip of

reasons for failure, and potential pitfalls in DHI interpretation provide valuable information in the reserve assessment process. Critical in the evaluation of DHI characteristics is the pertinent technical approaches employed and their calibration to known drilling results. However, even in development settings with a certain amount of well control for calibration, DHI interpretation requires a consistent and systematic evaluation procedure, including recognition of potential pitfalls and an understanding of the cause of the DHIs and their significance in reserve assessment.
Castagna, J., H. Swan, and D. Foster, 1998, Framework for AVO gradient and intercept interpretation: Geophysics, 63, no. 3, 948– 956, http://dx.doi.org/10.1190/1.1444406. Forrest, M., R. Roden, and R. Holeywell, 2010, Risking seismic amplitude anomaly prospects based on database trends: The Leading Edge, 29, 936–940, http://dx.doi.org/ 10.1190/​ 1.3422455. Roden, R., M. Forrest, and R. Holeywell, 2005, The impact of seismic amplitudes on prospect risk analysis: The Leading Edge, 24, no. 7, 706–711, http://dx.doi.org/10.1190/1.1993262. Roden, R., J. Castagna, and G. Jones, 2005, The impact of prestack data phase on the AVO interpretation workflow-A case study: The Leading Edge, 24, 890–895. http://dx.doi.org/10.1190/1.2056369. Rutherford, S. E. and R. H. Williams, 1989, Amplitude versus offset variation in gas sands: Geophysics, 54, no. 6, 680–688, http:// dx.doi.org/10.1190/1.1442696. U. S. Securities and Exchange Commission, 2008, Modernization of oil and gas reporting, December 31.

References

curs, weaker amplitude updip limits do not apply tions

proven pay in the same reservoir 9) Beware of large column heights for gas reservoirs 10) Data issues, such as prospect at edge of survey, complicate area estimates In development, seismic technologies such as spectral decomposition, neural networks, statistical approaches, and advanced seismic inversion techniques are often applied to help determine reserves. The transition from exploration to development and the use of seismic inversion requires the incorporation of all available geologic, seismic, and engineering data in an attempt to discriminate specifically between lithology, porosity, and fluid effects (Figure 11) and how these parameters relate to reserve calculations. Conclusions Over the past 11 years, 41 oil companies have contributed data for the evaluation of more than 200 drilled prospects (including assessment of the associated technical information and drilling results). The subsequent database developed from these assessments has enabled the identification of the pertinent aspects of direct hydrocarbon indicators and their impact on risking and resource/reserve determination. This database is composed primarily of exploratory wells (86%) and has direct relevance for computing resources in exploration. However, the lessons learned from this database are also pertinent for reserve assessment in development because an understanding of the most important DHI characteristics,
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Acknowledgments: The authors thank the member companies of the DHI Consortium for providing invaluable information necessary to develop the resulting interpretation process and prospect database. Corresponding author: [email protected]

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Geophysics in reserves estim ation

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

Stochastic volume estimation and connectivity analysis at the Mallik gas hydrate field, Northwest Territories, Canada
Camille Dubreuil-Boisclair and Erwan Gloaguen, INRS-ETE Gilles Bellefleur, Geological Survey of Canada Denis Marcotte, Ecole Polytechnique de Montréal

G

as hydrates located offshore and onshore beneath thick permafrost areas constitute one of the largest untapped natural gas resources. Yet, gas hydrate in place (GHIP) estimation at the scale of a field is not common in the scientific literature but is required to realistically assess the economical potential of specific accumulations. Progress in the last decade in Alaska and Canada has shown that gas hydrate accumulations beneath thick permafrost can be mapped at depth using conventional seismic attributes (Inks et al., 2009; Riedel et al. 2009). To evaluate the economic potential of gas hydrates in this environment, a test site at Mallik, Northwest Territories, Canada, was extensively surveyed (three-dimensional seismic, full set of logs in two wells, etc.) and a production test was realized in high gas-hydrate horizons. At Mallik, high P- and S-wave velocities, high acoustic impedances, and strong seismic amplitude reflections were all linked to sand-rich sediments with a high saturation of gas hydrates (Bellefleur et al. 2006; Riedel et al.). This relationship provides a strong basis for an integrated data characterization of this gas hydrate deposit. Dubreuil-B. et al. (2012) showed how results from Bayesian stochastic simulations combining well-log data and 3D impedances can be used to estimate the lateral and vertical heterogeneities of gas hydrates at Mallik Field and provide an in-situ GHIP estimate. In this study, the previous gas hydrate grade simulation results are used to provide a probabilistic estimation of the reservoir connectivity, which is of major importance for reservoir exploitation simulation as it directly impacts the planning of well patterns and well completion. We also com-

pare in-situ and connected gas hydrate volumes which indicate that particular attention or alternative production technology might need to be considered to recover gas hydrate not connected to the main continuous zones. Geologic settings and data The Mallik site is onshore the Arctic permafrost near the coastline of the Beaufort Sea, in the Mackenzie Delta (Figure 1). Three internationally partnered research well programs have intersected three intervals of gas hydrate and allowed successful extraction of subpermafrost core samples with a high concentration of gas hydrate (Dallimore and Collett, 2005). Gas hydrate was principally observed within coarse-grained sand and thin beds of sandy conglomerate. The gas hydrate intervals are up to 40 m thick and have high gas-hydrate saturation, sometimes exceeding 80% of pore volume in unconsolidated sediments with porosity ranging from 25 to 40%. Little to no gas hydrate was found in the fine-grained silt, dolomite beds, or thin coal interbedded layers. Well-log data. A full set of log data was measured at the Mallik 2L-38 and 5L-38 wells (Figure 1). Both wells cross the entire gas hydrate zone, from 850 to 1100 m, and reveal high P-wave velocities (>3000 m/s) in highly saturated gas-hydrate intervals confined in higher-porosity sand layers. Higher velocities in highly saturated gas-hydrate layers are explained by the icy-like structure of hydrates, which increases the stiffness of the sediment matrix. P-wave velocities sediments that are not hydrate-bearing typically range between 2000 and 2200 m/s.

Figure 1. Disposition of the 3D seismic data subset and wells 5L-38 and 2L-38.
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The deeper layer, zone C, is just above the gas hydrate stability zone (GHSZ) defined at a depth of about 1100 m. Zones B and A are shallower, between 900 and 1000 m. Zones C and B are observed on both wells whereas zone A is visible only on well 5L-38. The presence of gas hydrate in these horizons is confirmed by several well-log measurements (resistivity, P- and S-wave velocity, NMR). The logs of the total porosity, P-wave velocity, gas hydrate saturation and the grades at well 5L-38 are presented in Figure 2. The gray zones represent the highly saturated gas hydrate layers (zones A, B, and C). Contrary to conventional reservoir characterization that defines the saturation as the variable to infer, in this study, we used gas hydrate grades. The gas hydrate grades are obtained by multiplying the NMR-derived free-water saturation by the total porosity. The grade is a variable particularly suited for gas hydrate reservoir characterization, because in its natural form, gas hydrate is in a solid state. This variable describes the volume percentage of a solid component versus the entire volume. In addition, contrarily to saturation, grade is an additive variable, which allows up- and downscaling by simple averaging. 3D seismic data. The seismic data used are the upper 2 s of a 3D seismic reflection data set, acquired and processed in 2002. Unfortunately, the acquisition geometry was designed to image conventional hydrocarbons beneath the gas hydrate zones (deeper than 1100 m). The initial processing also focused on imaging the conventional gas-bearing structures rather than gas hydrate in the shallow part of the data. The data set was reprocessed to improve seismic imaging in the gas hydrate zone and to preserve the relative true amplitudes. The data used in this study are a subset of the 3D cube centered on wells 2L38 and 5L-38 and selected by Bellefleur et al. and Riedel et al. for detailed acoustic impedance inversion. The data set is composed of 41 × 41 traces, with an intertrace spacing of 30 m, covering an area of 1.44 km2. A time-to-depth conversion chart was built from a zero-offset VSP measured at borehole 2L-38. Gas hydrate reservoir modeling Bayesian gas hydrate grade simulation. Using the 3D inverted acoustic impedance data together with acoustic impedance and gas hydrate grade data from wells 5L-38 and 2L-28, multiple equiprobable 3D grade scenarios are simulated using the Bayesian simulation algorithm detailed in Dubreuil-B. et al. (2012). The proposed simulation algorithm is based on conventional sequential simulation approach. The approach also includes a Bayesian step consisting in multiplying a priori distribution of the grades by a likelihood function, linking acoustic impedance to gas hydrate grades. In this study, an in-situ petrophysical model is inferred from the well-log data, upscaled at the 3D vertical seismic scale (~3 m) to be consistent with the vertical seismic scale. Then, the joint probability density function (pdf) is calculated using a nonparametric kernel density estimator (Figure 3). Once many gas hydrate grade realizations are obtained, it is possible to infer the total volume of gas trapped within hydrates, for different cutoff values. However, from an exploitation perspective, the spatial continuity and connectivity of

Figure 2. Total porosity, acoustic impedance, gas hydrate saturation, and grades of logs of well 5L-38.

Gas Hydrate Grade Cutoff [×106m3] 0.15 Zones A and B Zone C Zones A, B, and C 714±114 613±74 1327±146 0.20 9±21 69±59 78±63 0.25 negligible 2±3 2±3

Table 1. Connected natural gas volumes (in Mm3) for zones A, B, and C, at three different grade cutoffs.

the layers rich in gas hydrates is of major importance and not provided by only the total gas volume. Thus, using the all gas hydrate grade simulations, this study focuses on connected volume estimations for different grade thresholds or cutoffs. Figure 4 presents a randomly chosen 3D gas hydrate grade realization using the proposed approach. All the realizations are constrained by the upscaled log and 3D acoustic impedance data. The two continuous gas hydrate horizons (zones B and C) detected on the log data are recovered, as well as the major northwest-southeast anticline structure (white dotted lines in Figure 4) documented in the Mallik area (Riedel et al.). In these horizons, gas hydrate occupies from 20 to 40% of the total volume. Volume estimation. To estimate the economic volume that could be recovered using technology that still needs to be defined, it is important to know the connectivity between each voxel. In this study, a stochastic connectivity analysis is computed using all simulated grade fields. Starting from the highest gas hydrate grade location measured at a well, in a given zone (A, B, and/or C), the six neighboring voxels are visited. The voxels having a grade value greater than a specific cutoff are considered connected whereas voxels with values below the cutoff are simply rejected (Figure 5). The procedure is repeated
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with all the previously accepted voxels until no voxels having a grade value over the cutoff are found in the neighborhood. Thus, the minimum connectivity corresponds to the size of a voxel, which is 30 m × 30 m wide and 3 m high. As a result from the connectivity analysis computed for each 3D gas hydrate grade scenario, the connected natural gas volumes can be estimated together with their uncertainty, for each layer, at different cutoffs. The natural gas volume is estimated assuming that at standard atmospheric temperature (20°) and pressure (1 atm) conditions, 1 m3 of solid gas hydrate is equivalent to ~164 m3 of free gas and considering that a clathrate has an occupancy ratio of about 0.9.

Results The connectivity analysis is computed on each of the 50 gas hydrate grade realizations: zone C alone, A and B and the three layers together. The connected volumes of natural gas trapped within hydrates are calculated at three different grade cutoffs: 15, 20, and 25% (Table 1). A cutoff of 0.15 corresponds to the grade value that devises family 2 from family 1 (Figure 3). The results show that zones A and B are composed of a large amount of connected grade values (around 15%) but contain few connected grade values greater than 20%. This is also visible on the realization presented in Figure 4 where zones

Figure 3. In-situ probability density function of gas hydrate grades and acoustic impedance. The approximate locations of the two families are shown.

Figure 5. Connectivity analysis methodology.

Figure 4. One 3D gas hydrate grade realization. The dashed white lines represent the northwest-southeast anticline structure and the horizontal black dashed line is the base of the gas hydrate stability zone (from Dubreuil et al. 2012).
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Geophysics in reserves estim ation

Figure 6. 3D view of connected volumes for one realization, for the three zones, at cutoffs of (a) 0.18, (b) and 0.2.

Figure 7. Plan view of two realizations of the connected voxels for zone C, at a gas hydrate grade cutoff of 0.2.

A and B cover a moderately extensive area of relatively low gashydrate grade values. The results confirm that zone C is the most laterally extensive gas hydrate layer and that it contains the highest grade. At a cutoff of 0.2, meaning that 20% of the volume of the voxel is filled with gas hydrates, the natural gas volume within the hydrate reaches 96 ± 59 × 106 m3. The connectivity of that zone often reaches the horizontal limits of the seismic data set, suggesting that this layer expands beyond the 1.44 km2 of data available for this study. A higher cutoff reduces significantly the connected volume area for all layers. A cutoff increase of 0.05%, going from 0.15% to 0.2%, lowers the connected volume by 98% in zones A and B, by 88% in zone C and by 94% overall. The connected volume also differs from the in situ total volume previously calculated in Dubreuil et al. (2012). At a cutoff value of 0.15, the connected volume for the three layers decreases by 17% or 276 × 106 m3 when compared to the in situ volume. This suggests that special attention or different
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production techniques might need to be considered to recover gas hydrate not connected to the main zones. The reasons explaining the breakup in connectivity are not known but we presume that changes in sedimentary facies are a significant factor. The connected volumes for one gas hydrate grade realization, at cutoffs of 0.18 and 0.2, are presented in Figure 6. The figure clearly shows the connected volume reduction as the cutoff increases. Zone A is intersected by 5L-38 and extends eastern of that borehole. Zone B covers a small area connected between 2L-38 and 5L-38. The image shows clearly the greater connectivity and the higher grades of zone C compared to the two shallower zones. These results are in agreement with acoustic impedance results also demonstrating that zone C is the most laterally continuous (Riedel et al.). The plan view of two realizations of the connectivity for zone C, at a cutoff of 0.2, is presented on Figure 7. This cutoff is chosen because it represents only the highly saturated gas

Geophysics in reserves estim ation

Figure 8. Connectivity probability for each voxel, for each zone, evaluated at a cutoff of 0.18.

hydrate voxels (family 2). The figure shows the great variability of the connected patterns of this gas hydrate layer. It also highlights the possibility that the layer extends to the northwest where both realizations reach the limit of the data area. Finally, Figure 8 shows the probability of the voxels to be connected to the well at a grade cutoff of 0.18. The probability volume is obtained from the connectivity analysis of 50 simulations. Areas with higher probability represent the connected volume that could be naturally solicited during a production from either 2L-38 or 5L-38. Results show that the uncertainty is greater in zones A and B. Again, zone C has a high probability over a much greater radius than zones A and B. Conclusions The strong link existing between acoustic impedance and gas hydrate grade allows modeling gas hydrate in place using a covariate simulation scheme. Bayesian stochastic simulation was used to determine the spatial heterogeneity of gas hydrate grades and assess the connected natural gas volumes at the Mallik site, at different grade cutoffs. Results show that the deeper zone (zone C) has higher grades and higher connected gas volumes than zones A and B. Zone C is also much more connected over all the studied area. At a cutoff of 0.18, the connectivity of zone C reaches the limit of the seismic data suggesting that this layer could still be connected beyond the 1.44 km2 study area. The connectivity analysis confirms that zone C, being just above the gas hydrate the stability zone, is the most favorable gas hydrate layer for future reservoir engineering developments considering its large lateral and well-connected extension and its high gas hydrate grade.
References
Bellefleur, G., M. Riedel, and T. Brent, 2006, Seismic characterization and continuity analysis of gas-hydrate horizons near Mallik research

wells, Mackenzie Delta, Canada: The Leading Edge, 25, no. 5, 599– 604, http://dx.doi.org/10.1190/1.2202663. Dallimore, S., and T. Collett, 2005, Summary and implications of the Mallik 2002 gas hydrate production research well program, in S. Dallimore and T. Collett (eds.), Scientific results from the Mallik 2002 gas hydrate production research well program, Mackenzie Delta, Northwest Territories, Canada: Geological Survey of Canada, Bulletin 585. Dubreuil-B., C., E. Gloaguen, G. Bellefleur, and D. Marcotte, 2012, Non-Gaussian gas hydrate grade simulation at the Mallik site, Mackenzie Delta, Canada: Marine and Petroleum Geology, 35, no. 1, 20–27, http://dx.doi.org/10.1016/j.marpetgeo.2012.02.020. Inks, T. L., M. W. Lee, W. F. Agena, D. J. Taylor, T. S. Collett, and M. V. Zyrianova, 2009, Seismic prospecting for gas-hydrate and associated free-gas prospects in the Milne Point area of northern Alaska: AAPG Memoir, 89, 541–554. Riedel, M., G. Bellefleur, S. Mair, T. Brent, and S. Dallimore, 2009, Acoustic impedance inversion and seismic reflection continuity analysis for delineating gas hydrate resources near the Mallik research sites, Mackenzie Delta, Northwest Territories, Canada: Geophysics, 74, no. 5, B125–B137, http://dx.doi.org/10.1190/1.3159612.

Acknowledgments: We acknowledge the international partnership that undertook the Mallik 2002 Gas Hydrate Production Research Well Program: the Geological Survey of Canada (GSC), Japan National Oil Corporation (JNOC), GeoForschungsZentrum Potsdam (GFZ), U.S. Geological Survey (USGS), India Ministry of Petroleum and Natural Gas (MOPNG), BP/ChevronTexaco/Burlington joint venture parties, U.S. Department of Energy (USDOE). The first 2 s of a 3D seismic reflection survey shot in the Mallik Field area in 2002 has been made available to the Mallik science program through partnership with the joint venture parties, BP Canada Energy Company, Chevron Canada Resources, and Burlington Resources Canada. Corresponding author: [email protected]
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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

Case study: Using seismic inversion to constrain “proved area” definition
K. Eastley, P. Unstead, Shell Development Australia H. J. Kloosterman, Shell International BV

t the end of 2008, the U.S. Securities and Exchange Commission (SEC) published new rules regarding the determination of oil and gas reserves. The updated requirements state that the drainage area for the life of the field must be based on all available engineering and geoscience data. This paper presents a recent case study in which seismic data were used extensively in support of the reserves booking. We describe how seismic and inversion data were used to discriminate between “good” and “poor” reservoir away from wells and how these data were ultimately used to demonstrate reservoir continuity (as a function of “good” rock distribution) as the basis for “proved area” determination. Well data (both log and core), well tests, and seismic data were integrated to constrain the reservoir “tank”, establish continuity of reservoir sands away from and between wells, and to demonstrate economic producibility across the proved area with reasonable certainty. Key elements of the adopted workflow strategy were: • Understand the depositional model and facies • Evaluate rock properties at the wells • Validate the quality and predictive capability of the seismic inversion • “Blind” tests in high- and low-impedance areas • Evaluate inverted properties around the wells • Create probability distribution functions (PDF) of inverted properties (e.g., impedance) for “good” reservoir (wells) and “bad” reservoir (wells) • Demonstrate the ability to discriminate on the basis of the inverted properties—for example, determining if P10 from “bad reservoir (wells)” is greater than P90 from “good reservoir (wells)”

A

• Establish impedance cutoffs for discriminating good and poor reservoir (wells) • Apply impedance cutoffs to inverted seismic to establish areas of high confidence about good quality reservoir (and poor reservoir quality) Understanding the depositional model and facies The depositional model is constrained by 11 wells with combined core coverage of greater than 500 m. The depositional model has a high net-to-gross (> 80%) and large turbidite system with feeder channels that feed and incise into a background of lobes. The lobes consist of two distinct facies, reservoir sand facies and lobe fringe and abandonment facies (typically interbedded silts and shales). Two distinct lobe types (with respect to reservoir properties) are recognized from well data and are termed lobe A 1/2 and lobe B 1/2 (with 1 and 2 synonymous with reservoir sand and abandonment facies). The workflow for modeling properties reflects the geological model and key facies (Figure 1a). The modeling strategy involves defining the background of lobes (A/B reservoir sand) into which fringe and abandonment facies are emplaced (A/B lobe 2 facies). Finally channel elements are then emplaced into the background lobe facies (Figure 1b). Evaluation of rock properties at the wells All modeled facies have a significant range of properties. Channels and A lobe facies have better average reservoir quality than B lobe (Figure 2). The modeled facies are used to constrain the distribution of reservoir properties The definition of good and bad reservoir (wells) is accomplished by applying the following cutoffs: water saturation

Figure 1. (a) Conceptual depositional model. (b) Layer from PETREL model showing key architectural elements.
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Figure 2. Porosity versus permeability crossplot highlighting the distinct “good” and “bad” property clusters.

Figure 3. Application of “good reservoir” cutoff parameters (water saturation < 25%, porosity > 10%, and permeability > 1 mD) clearly distinguishes between “good” and “bad” wells in this case study.

< 0.25, porosity > 0.1, and permeability > 1 mD. These are broadly consistent with well-test results. In summary, channel and A lobe 1 facies are typically good facies and A lobe 2, B lobe 1 and B lobe 2 are bad facies. When applied to the 11 wells of

the case study, the distribution of good and bad reservoir by well is seen (Figure 3). There is a clear distinction between good and bad wells. Good wells typically have about 70% of good reservoir rock while bad wells have < 20% good rock.
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Figure 4. Average P-impedance values extracted from the interpreted well test radii of investigation for the “good” well (1000 m) and the “bad” well (300 m) demonstrate the clear distinction between the wells.

Figure 5. P-impedance distributions for the “good” wells and “bad” wells highlight the ability of seismic inversion to discriminate between “good” and “bad” wells with reasonable certainty.

Seismic inversion workflow The seismic inversion objective was to test the capability of using inversion output (for example, average impedance) to discriminate between good and bad facies (wells). The following workflow was adopted:
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• Identify good/bad reservoir and define good/bad wells using all available wells • Extract the average impedance values within a 300-m area around the well (Petrel grid size) • Create PDFs of average impedance values for the identified good and bad wells

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Geophysics in reserves estim ation

Figure 6. Average P-impedance distributions for main reservoir interval highlighting the well locations (including locations of blind test wells). Discrimination between the “good” well locations (2–5, 7 and 9) and “bad” well locations (1, 6, 8, 10, and 11) is clear.

Figure 7. Summary of wells with a good fit to the inversion model. Only one well is considered to have a marginal fit. Data from this well location (7) are locally influenced by the presence of fault shadows impacting the amplitude and hence inversion output.
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Geophysics in reserves estim ation
of the good wells and P10 of the bad wells and that there is a relatively small impedance “transition” zone between good and bad wells. Clearly the inversion has the capability to discriminate between good and bed wells with reasonable certainty. Figure 6 is an average impedance map that includes the locations of the 11 wells previously classified as good or bad wells. Well 1 (bad) and well 4 (good) were not used as input into the seismic inversion and show a good fit to the inversion model. Figure 7 is a repeat of Figure 6 but in this case those wells with a good fit to the inversion model are orange while those wells with a “marginal” fit to the inversion model are white. Ten out of the 11 wells (including the blind tests) fit the model, indicating that low impedance is diagnostic of good rock. Furthermore the marginal fit (high impedance value comFigure 8. Distribution of good and bad rock after applying cutoffs (see Figure 5). pared to good well) is influenced by the impact of a local “fault shadow” upon the seismic response. In summary, there is high confidence that the • Identify P10 and P90 average impedance values for the can discriminate between the good and bad reservoir. inversion good and bad well PDFs to establish cutoffs Finally, upon applying the appropriate impedance cutoffs to • Apply cutoffs to the average impedance map to define an the inverted seismic, we establish those areas of high confidence “area” of high confidence of good reservoir presence and of good quality reservoir and its continuity and those areas of continuity poor reservoir quality. Figure 8 highlights the distribution of the • Conduct blind tests in high- and low-impedance areas good (red) and bad (light blue) rock. In summary, the workflow adopted has used all available An additional objective of the study was to understand the geoscience and engineering data in an integrated fashion to concontrols on hydraulic connectivity and economic producibility strain the lateral extent of the “proved area.” The well-test data of the reservoir and how best to constrain the distribution of clearly highlight that parts of the reservoir that are “producible” this reservoir to an area of high confidence. As discussed earlier, at “commercial” rates. Field development studies demonstrate good and bad wells are identified by applying the following that recovery is economic under the proposed development cutoffs: water saturation Sw < 0.25, porosity > 0.1, and permeconcept (wells and their completion and the facilities to extract ability > 1 mD. An important step was to link the good and process the gas). and bad reservoir (wells) to available production tests. The seismic inversion has been used to extrapolate the horiTwo production tests were available, one from a good well zontal or lateral extent of the proved area into adjacent undrilled (well 2) and one from a bad well (well 1); these wells have disportions of the reservoir that can, with reasonable certainty, be tinctly different P-impedance signatures. judged to be continuous with it. This is consistent with the reTo constrain the P-impedance data by the well-test data quirements of constraining the proved area. (indicative of producibility and hydraulic connectivity), the radii of investigation from the well tests were interpreted as 1000 m for the good well (average well test permeability = 45 mD) Conclusions and 300 m for the bad well (average well test permeability = This case study demonstrates that seismic inversion can in principle assess the presence of good quality reservoir in support of 1–5 mD). Figure 4 summarizes average P-impedance values within reserves estimates. This requires as a minimum: the radii of investigation for the good and bad wells, respectively. The histogram clearly demonstrates the ability to dis- • Good understanding of the depositional model and facies criminate between good and bad rock; furthermore, the link • Good understanding of rock properties and their distribution by facies to well-test data indicates that the P-impedance is able to disunderstanding of predictive capability of the seismic • Good criminate the distribution of reservoirs with excellent hydraulic inversion connectivity and economic producibility. Figure 5 shows the PDFs for all “good” and “bad” wells. • Blind tests in high/low impedance areas to increase confidence The key observations are that there is no overlap between P90
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Geophysics in reserves estim ation
This case study illustrates the key role seismic inversion played in an integrated geoscience and engineering analysis that underpins a resources and reserves estimation and classification.
Acknowledgments: The authors thank Shell Development Australia for supporting the work and permission to publish this paper. We also acknowledge the support and contributions from Laurent Bourdon, Rebecca Day, Marcia Edgley, and Cherie Sims. Corresponding author: [email protected]

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SPECIAL Geoph SECTION: ysics in Ge ro es pe hr yv se is c se s it ni m re as te ir ov ne s e s t i m a t i o n

Strategies in geophysics for estimation of unconventional resources
Eric von Lunen, Stephen Jensen, and Jennifer Leslie-Panek, Nexen

he rise of unconventional resource plays to prominence in the oil and gas industry has presented geophysics with a set of unprecedented challenges, chief among which is the problem of resource and reserve estimation. Instead of the traditional concerns with trap mapping, spill points, and degree of fill, unconventional resource plays require information on reservoir quality, fracability, fracture networks, and the stimulated rock volume (SRV) resulting from fraccompletion programs. Figure 1 shows the critical differences in risk assessment between traditional and unconventional systems. The quantification of the “deliverability system” is the principal area which geophysical methods must address. These requirements lead to a reliance on seismic inversion, attributes such as curvature and coherence, and microseismic data. Too, the low porosity of many unconventional reservoirs demands greater trace-to-trace fidelity and low noise in the geophysical data; premium acquisition programs and processing work flows are needed, together with strict quality assurance standards. The current perception of many geoscientists and petroleum engineers is that for unconventionals the geophysical tools struggle to meet the standards of reliability expected under the Petroleum Resources Management System (PRMS) (see Lorenzen et al. in this special section). However, the techniques are evolving rapidly. The purpose of this paper is to review the state-of-the-art and to describe some of the technical initiatives which are being pursued to advance the use of geophysics in unconventional resource estimation. In addressing the problem of resource estimation and the deliverability system risks, several strategic principles are critical for the acceptance of geophysical methods in a more prominent role. These include:
1) The technology proposed must be reliable in the percep-

T

tion of the geoscience community and documented in professional technical papers. 2) Standards of acquisition must be documented and rigorous, such that a third-party auditor can readily quantify the uncertainties in resource estimates and have sufficient basis to confirm them to a regulatory body such as the SEC. 3) Standards of acquisition and processing must include data standards, instrument specifications and tests, and QC&A documentation, as well as documents showing relevant parameter testing or modeling. 4) Processing workflows and algorithms need a sufficiently detailed technical description to permit a reviewer to assess the adequacy and appropriateness of the application. In other words, black boxes are unacceptable. 5) A guiding principal in resource estimation and deliverability workflows is a thorough “fit-for-purpose” assessment of technologies and their QC&A standards.
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Figure 1. Critical differences between traditional and unconventional resource petroleum systems. In unconventional resource exploitation, the reservoir natural fracture network and the completion-induced fracture network connectivity become critical risk factors. The deliverability risk which must be described includes rock properties, principal stress fields, and effective porosity and natural fracture network interconnectivity. The risk also includes a technical assessment of the completion method chosen to enhance the resource deliverability (courtesy of Nexen).

Geophysical workflow Given the nature of the resource estimation problem, the procedure for unconventional plays is necessarily much the same as that for other plays, but with different details and a different emphasis (see Kloosterman et al. in this special section). We outline here a general workflow, mentioning significant data requirements and concerns along the way. Defining the container. With unconventional resource plays, the geophysicist knows from an early stage where the hydrocarbons are; there is no question of mapping degree of reservoir fill. Nevertheless, resource estimation still begins by

Geophysics in reserves estim ation

defining the boundaries of the reservoir unit. This process is essentially the same as that followed in mapping a conventional play, and little need be said here in elaboration; established geophysical techniques constitute reliable technology in PRMS terms. Reservoir units, especially coal beds, are often interpretable markers; where this is not the case, reservoir boundaries can be phantomed by means of isopachs and isochrons from usable horizons. The greatest use for conventional horizon mapping is in the practicalities of field development: well prognosis, bit steering, and drilling hazard (e.g., fault) avoidance. In some shale gas plays, faults and diagenetically enhanced open fracture zones are to be avoided, as these act as conduits for water to break through the seals bounding the resource container and consequently drowning the wellbore. Shale gas reservoirs can exhibit a subtle reflective character and may not be mappable by traditional horizon picking. In such cases, inversion volumes must be used to define reservoir boundaries. The effects of noise are serious in regard to elastic inversion. Acquisition and processing workflows, as well as additional geologic constraints input into elastic inversion, need to be carefully selected from the “fit-for-purpose” perspective. Establish reservoir continuity. Unconventional reservoirs can be surprisingly heterogeneous, and a failure to recognize such heterogeneities has led more than one project to economic grief. For ex- Figure 2. (a)The resource hydrocarbon volume can be adjusted based on seismic waveform characterization. In this example, seismic waveform characterization ample, oil sands reservoirs often contain channel is modeled from limited well control and then used to decompose the reservoir systems whose fill can be nonreservoir. Gas shales interval into seismic detected facies related to rock properties. (b) Six classifications can contain nonreservoir facies that also act as frac representing different resource volume (in terms of OGIP by startigraphic interval) barriers. These volumes cannot contribute to esti- and detectable rock properties are used to form “consistent opinions” about the mates of original gas in place (OGIP) or original deliverability factor of the seismic facies. (Note is this particular case the limit to this technical application was ultimately controlled by “fit for purpose” acquisition oil in place (OOIP); as well, they can render res- and processing design decisions) (courtesy of Nexen). ervoir zones inaccessible to drainage. In Figure 2, we show an example where OGIP was reduced because of a calibration. The subtle nature of the features being mapped nonreservoir marl facies. All these nonreservoir zones must can make calibration and consistent interpretation difficult. Predict reservoir properties. It is at this point that resourcebe identified and mapped and their volumes subtracted from the gross reservoir volume before a development plan with play geophysics differs the most from conventional-play geoproject economics can be prepared. None of this is essential- physics. We seek sweet spots in a known reservoir, and our ly different from traditional reservoir evaluation; the prob- work must contribute to an optimal development design. The lems arise in connection with the often low-contrast nature tools of choice are multicomponent 3D surveys, elastic inof these features in unconventional reservoirs and with the version, and, for shale gas, microseismic data, together with subtle character expression of lithology and mechanical stra- multi-azimuth VSP surveys. In shale gas projects, geophysics is called upon to charactigraphy. Statistical approaches can be of value. The geophysical tool kit for dealing with reservoir conti- terize reservoir properties before and after the frac program. nuity and compartmentalization includes many of the com- Before the frac program, engineers want to know the in-place monly used attributes. Inversion products such as Poisson’s resource, the fracability, faults to avoid, variations in princiratio and Young’s modulus may be needed to characterize pal stress direction, the location and orientation of natural geobodies; coherence volumes, character-based facies clas- fracture systems (which can add complexity to a fracture patsification, and such proprietary techniques as Ant Tracking tern), and the locations of barriers. After the frac program, are useful. These techniques are reliable technology under the engineers want to know the SRV, the overlap between PRMS-AG (Guidelines for Application of the Petroleum Re- fracture patterns, and the location or distribution of proppant sources Management System), but they require local regional emplacement, all of which are critical input for a geophysical
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Figure 3. The hybrid combination of stochastic models and visual classified textural effects can lead to a more robust description of the OGIP volumes ultimately accessed within an SRV, and its internal flow unit system which delivers hydrocarbon to the wellbore. In this example, the three principal stress components and pore pressure are the same for each of these possible frac geometry outcomes; yet, we note the fracture intensity, size, connectivity, aspect ratio, and flow efficiency could be significantly different. A critical element of constraint information for this stochastic model is comparison of the power law relationships between microseismic events, seismic curvatures, and faults observed.

recovery factor calculation. In addition, engineers want to know how to improve the frac program to achieve a greater recovery factor and to reduce cost. Resource-play unconventional resource geophysics, then, has two principal tasks. The first is to characterize the state of the reservoir before completion stimulation. In both shale gas and tight oil projects, hydrocarbon recovery is dependent on the existence and connectivity of natural fractures. Often the fairway of interest for the exploitation of such resources is found by mapping these natural fracture systems. In addition to the techniques discussed above, topics of current research interest for defining fracture-related producibility are velocity and amplitude anisotropy combined with shearwave birefringence. In coal-bed methane, the characterization of the upper and lower bounding rock and its effectiveness as a seal barrier isolating the resource is an important initial objective. Geophysical tools which can relate rock types and natural fracture networks to in-situ stress and rock properties such as Poisson’s ratio, Young’s modulus, density, and rigidity become critical in planning the resource stimulation plan. This information comes from the 3D survey, its attributes, and its inversion products.
1092 The Leading Edge September 2012

The second task is to evaluate the modification of the target reservoir into a state that permits economic production. This requires us to monitor the stimulated rock, identify bypassed resource pay, verify the resource confinement after stimulation, and predict or forecast the hydrocarbon delivery success from stimulation-induced changes in observed geophysical characteristics. In oil sands work, steam-assisted gravity drainage (SAGD) projects require geophysics to track the growth of steam chambers with 4D seismic surveys, look for bypassed bitumen, and examine the integrity of the seal barrier to prevent hazardous chamber failure and the loss of resource that occurs when the chamber bursts to surface. In shale gas exploitation, we must determine the extent of both the natural fracture system and the frac-induced fractures, the likelihood of achieving the desired SRV based on rock mechanical properties, the initial state of stress both vertically and laterally, and the effectiveness of seals and barriers to isolate the producing rock media. These data are provided or might be provided in the future, with improved analysis, by microseismic data. Integrate with other data analyses. This stage in the work flow is critical for unconventional resource geophysics; the

Geophysics in reserves estim ation

more so in that tests and comparisons of new techniques are needed to evaluate their usefulness. Coherence techniques, curvature, horizontal anisotropy, and edge-detection algorithms can predict fracture zones, but they must be compared with core and image-log data from horizontal wells to calibrate them. Elastic inversion products can be used to predict in-situ shale gas resources and reservoir brittleness, but only after careful comparison with constraining well data. Estimates of SRV, proppant emplacement, and recovery factors can be generated only after comparison with production logging and decline curves. In unconventional plays, the geophysicist at present is perceived more as a supporting player than in traditional exploration and development, and geophysical interpretation products and predictions sometimes encounter skepticism. The standards for reliability, repeatability, and accuracy are high. This should clearly be the future direction of R&D efforts in order for geophysics to contribute seriously to resource and reserve estimation. Data requirements, processing and analysis standards, and emerging technologies In this section, we discuss the requirements and associated problems with geophysical data acquisition, processing, and analysis for unconventional resource assessment. We also describe a number of emerging technologies and innovative procedures being tested in the Horn River Basin (HRB). 3D seismic acquisition, processing, and elastic inversion. For unconventional resources it is critical that seismic data be evaluated for: • • • • • Acquisition footprint Vertical and horizontal resolution Spatial accuracy of seismic character Fidelity of the seismic trace and seismic gather Clarity

Figure 4. This Bakken oil resource play example shows a complex pattern of microseismic events. The cloud is essentially an SRV encompassing six hydraulic frac completed stages. Each stage as it was pumping created a volumetric region correlated with the pumping program executed. The figure shows each stage highlighted with a different color, as well as the shared spatial subregions commonly shared or overlapping between stages. The overlapping regions would be our first guess to describe regions of fracture network interference. The question becomes which portion of this SRV is delivering significant hydrocarbon production to the horizontal wellbore (courtesy of Transform Inc.).

• Seismic receiver signatures must have minimal distortion related to spread geometry, a critical element in subtle AVA and elastic inversion work. • Careful attention to sensor-to-ground coupling, as well as to background noise, is needed. • Provision of adequate trace fold as a function of both offset and azimuth must be ensured. The specifics of acquisition and processing design is an extensive body of literature of which Berkhout (1984), and Vermeer (1990) are recommended. As noted above, the variations in reservoir parameters that we attempt to map in unconventional resource plays can be subtle. This is particularly true for shale gas, with porosities often less than 5%, and with anisotropic organic material content of 2–10%. It is easy for acquisition artifacts or other noise to degrade the signal; it is essential that we do not trade

We emphasize trace and gather fidelity from several key perspectives: • Seismic source bandwidth should be greater than three octaves and repeatable from shot-to-shot; the amplitude spectrum should be flat to better than 12 db.

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quality of data for quantity of data. A bargain-basement survey will likely prove useless in the inversion stage. To begin, the survey must provide not only full fold over the area of interest, but also a full-offset range and a full set of azimuths. This means that tail spreads must be generous and the survey outline must be simple. The acquisition patch should be symmetric, to provide equal-offset ranges both inline and crossline. Source and sensor trace density in shooting should be chosen with horizontal anisotropy processing in mind; to sparse a spread will, for example, limit the choices for sector processing and require either overlap or interpolation. Seismic trace interpolation is not generally recommended, except to regularize the seismic trace spacing and grids as required for migration and inversion. Maximum offset should be generous, because the far offsets contribute disproportionately to the inversion, especially for density. We recommend the use of three-component phones to provide a mode-converted shear-wave section. We also prefer vibroseis as a source to maintain a robust high-fidelity wavelet. A stable source wavelet is an essential element in an accurate elastic inversion. Joint P- and S-wave 3D surveys can offer advantages. Although mode-converted S-wave spectra often have less than half the frequency content of the associated P-wave spectra, a direct measurement of the anisotropic S-wave field at the reservoir interval is useful for mapping fracture networks and maximum stress. Curvature features can be mapped with two different wavefields. Weak P-wave markers may be easier to image on shear sections. The measurement of class 2 AVA responses is more accurate when actual shear data are used in a joint P and S elastic inversion. We acquire joint P-wave and S-wave multi-azimuth walkaway VSPs to provide anisotropy information for seismic processing and to enable a precise calibration of the P-wave and S-wave time-depth relationships. Given the problem of parallel P-wave and S-wave statics, we shoot a suite of shallow 2D refraction lines using both P-wave and S-wave sources to supplement the static solution from the 3D survey, which in general lacks the near offsets needed to resolve the nearsurface statics. In the HRB, airborne EM has proven useful in mapping Pleistocene channel systems, a key input to the seismic statics solution. It is necessary to rigorously remove noise, especially coherent noise, before attempting an inversion. The Horn River Basin is characterized by large-amplitude, high-velocity interbed multiples which often have velocities as fast as the primaries they overlay. These multiples are generated by massive carbonate units in the Mississippian and Upper Devonian. We have found that the usual Radon transform techniques must be supplemented by a deterministic demultiple approach to attack these multiples successfully. In this case, attempts at elastic inversion with inadequate multiple removal resulted in volumes riddled with obvious artifacts. (For details on deterministic demultiple techniques, see Verschuur, 1991; Kelamis and Vershuur, 2000; Yilmaz, 2001; and Dragoset and Jericevic, 1998). It is not unusual to find that 3D inversions fail to ad1094 The Leading Edge September 2012

equately resolve short-wavelength variations in elastic parameters and to capture the range of variation inherent in the data. Stochastic inversions can address this to some degree and usually provide better vertical resolution. Software for this kind of inversion is undergoing rapid improvement, and the QC of the process presents new challenges. We find it valuable to involve those who are to perform the inversion in the QC of the basic processing, to ensure that the data are “fit-for-purpose.” VSP surveys for unconventional reservoirs. Vertical seismic profiles (VSP) provide useful information in both conventional and unconventional reservoirs. They are the main calibration point for all seismic data, 2D and 3D, as they provide a direct tie between the well-log-derived synthetic and the seismic trace, through the zero-phase corridor stack. As such, they provide a direct depth-conversion point from the seismic data to the well data which is crucial in converting seismic data from the time domain to the depth domain. In addition, this is useful when integrating seismic time data with microseismic depth data. Time-to-depth conversion in thick, highly anisotropic rocks (> 15% over a 100-ms interval) often introduces a spatial error in linking seismic inversion volumes to well control and microseismic hypocenters. Surface seismic usually does not provide sufficiently accurate measures of this error. Hence, we emphasize borehole seismic methods in unconventional media. As well, pre- and postcompletion VSPs have demonstrated significant velocity and azimuthal anisotropy changes in the reservoir resulting from frac programs. As mentioned previously, interbed or peg-leg multiples are a problem in certain basins and may create interference in the reservoir section of the seismic data. These multiples must be removed in order to obtain a reliable inversion product for reservoir parameters. VSP surveys are the best method to identify the multiples in the seismic section, as they are readily distinguished on the downgoing section as well as the corridor stack (Hampson and Mewhort, 1983). This information can then be input into deterministic demultiple seismic processing routines prior to inversion for optimal results. Walkaway or multi-offset VSP surveys can provide an estimate of the VTI anisotropy due to layering of the overburden. These VTI anisotropy parameters are useful inputs for building velocity models for forward modeling, depth migration and also microseismic data analysis (Tsvankin et al., 2010). Walkaround or 3D offset VSP surveys are capable of providing azimuthal (HTI) anisotropy parameter estimates which are often related to vertical fractures. These natural fractures are important to completion programs in shale gas reservoirs. Microseismic data for unconventional resource estimation. Gas shales have permeabilities on the order of 100 nanodarcies and tight oil reservoirs are some tens of microdarcies; these numbers severely limit the distance that gas or oil molecules can diffuse through rock during the lifetime of a project. Achieving an adequate recovery factor is therefore entirely dependent on having a complex network of fractures in the rock, whether natural or frac-generated. Traditionally the

Geophysics in reserves estim ation

Figure 5. Concepts of crack mode failure and simplified relationship to moment tensor presentations (courtesy of Urbanic, 2010).

recovery factor has been estimated from production decline curves and reservoir modeling. Because the production life of shale gas wells can be on the order of years, the accuracy of recovery factors and production forecasts generated by model curve matching is low until much time has passed. The microseismic technique offers an alternative approach for the assessment of reservoir deliverability and the recovery factor. The first attempts at this approach were presented nearly seven years ago by Mayhofer et al. The results were encouraging but still preliminary. Nevertheless, microseismic data are the geophysical tool of choice for estimating stimulated rock volume, future production, and recovery factor because it is the only technique that can directly observe the

creation of a drainage network within the stimulated resource container. Microseismic data can provide the reservoir engineer with critical inputs to the reservoir simulation model, including SRV and an estimate of frac complexity, enabling a better and quicker decline curve match. Recent case histories indicate that the microseismic technique is making advances in establishing itself as fit-for-purpose as an information source for reservoir simulation and production history matching. From displays of microseismic hypocenters, we attempt to determine frac-wing symmetry, azimuth relative to principal stress, and frac height, width and length (Figure 3). These observations are then compared with the time-synchronized frac-pumping curves. Two issues emerge quickly from analysis of these displays. First, the hypocenter locations produced from the same raw data vary substantially from processor to processor. Second, the hypocenters are often not located with sufficient precision to represent the simulated volume and drainage network. One has to admit that the current microseismic acquisition standards are poor, processing can be inconsistent, and interpretation lacks credibility. These are challenges that the geophysics industry is currently addressing. The basic task of accurately locating event hypocenters is the first hurdle to overcome. There are three interwoven aspects of the problem that must be dealt with: first, designing an adequate acquisition geometry for the survey, whether a downhole or a surface array; second, selection of valid and robust processing algorithms; and third, detecting a credible

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number of events to produce a statistically valid interpretation. The merits of surface versus downhole arrays are debated in the literature; we can employ both in the HRB. The HRB is presently being developed with large drilling pads (18 or more wells) and horizontal borehole laterals of up to 2200 m. We model proposed arrays configurations to provide sufficient solid angle to ensure accurate positioning. Downhole arrays connected by “whips” help to achieve the required array length. In processing, we track the probable error of each location from start to finish and store it in the database. We find that inadequate velocity models are a great source of position error. 1D velocity models are grossly inadequate; 2D velocity models including anisotropic may be workable. But an anisotropic velocity model which incorporates thin layering and structure is much to be preferred. Another critical requirement is a time break for perf shots which is accurate at seismic time scales. This means, among other things, only one master clock on a well site. With these changes in standards, we have reduced our vertical positioning uncertainty to less than 10 m at a depth of 2500 m. The third problem is to record and position sufficient microseismic events to statistically validate interpreted features. The SRV needs to be properly sampled. We currently process more than 750 events per frac stage and more than 30 events per 10-minute time window of the pumping process. Experience shows that if an SRV does not have a statistically significant population of events, cluster feature extraction will be misleading and inconsistent (Hendricks et al., 2012). One can rarely predict a reasonable production history by combining the SRV with an arbitrarily assumed discrete fracture network (DFN). Maxwell (2010) describes three key elements within the SRV. The first is the rock volume ruptured by the hydraulic fluid during pumping operations. The second is that part of the ruptured rock in which the fractures are kept open by proppant or which remain open under the stress induced by the completion. The third is the rock volume which supports flow into the wellbore in a reasonable time frame. Details of these three elements are needed for reservoir simulation models and must be provided by the interpreted features of the SRV hypocenter cloud. Geophysical approaches utilized by DFN shale gas models fall into four main types. These four approaches are dependent on the interpretation of the boundary edge of the SRV. Most work to date treats the boundary edge as a “shrinkwrap” skin encompassing the observed microseismic events (MSE). The SRV can be further refined based on additional MSE attributes. The first approach describes a hydraulic fracture treatment as a biwing fracture zone characterized by height, length, width, azimuth and symmetry. These visual measurements are readily taken from microseismic “dot” or location patterns. In a multistage program in a horizontal wellbore, each stage is summarized in terms of its own SRV defined by its own hypocenter cloud. This style of SRV description is rapidly being replaced by more sophisticated methods.
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A second common method of DFN characterization is to use the MSE observations statistically to derive fracturenetwork parameters. In this style of characterization, we describe a fracture network in terms of volume-based fracture parameters such as number of fractures per cubic meter, or the number of fractures for a given orientation, fracture size, and aperture. Rodriguez (2012) describes a method for the stochastic prediction of undetected MSEs which can be combined with a power-law approach to joint size and intensity distribution within the SRV. A rapidly emerging method is to allow the DFN parameters to vary for different SRVs. We allow the wellbore global SRV, individual stage SRVs, and SRV overlap zones to be intrinsically different. These are interpreted from overlapping microseismic event clouds. Within each decomposed volumetric portion we must define an observed or probabilistic fracture network. This concept presents challenges to DFN modelers in scaling relationships for each subzone’s permeability and interconnectivity. (Figure 4 displays five such stages.) Finally, if we consider the limitations of each of the previous descriptions of MSE for DFN characterization, a fourth or hybrid methodology could be implemented. In some situations the internal fabric or texture image is highly suggestive of drainage pathways (Geiser et al., 2012). More often, the internal fabric yields a visual image of shades, which we interpret to represent a more likely style of hydraulic fracture network (Vermilye, 1998; Vasudevan, 2011).We can characterize the SRV associated with the multistage horizontal wellbore in terms of detected flow pathways which drain or deliver hydrocarbons from stochastically described subzones. The subzonation is based on mechanical stratigraphy and a probability estimation of small-to-medium fractures which are scaled to describe local connectivity of the fracture-induced network. Current research on moment tensor inversion of event first motion appears likely to produce useful inputs to the DFN. Moment tensor analysis provides data on the following: • Opening and closing fractures • Orientation of the failure surface • Classification of events relative to three mechanisms of failure: volumetric, double couple shear, and compensated linear dipole • Stress /strain relationships as the frac process is completed • Classification of events as mode I, II, or III failures (Figure 5) Moment tensor inversion requires multiple arrays, whether surface or down hole, in order to obtain adequate solid angles. 4D seismic. The use of 4D seismic in SAGD projects has been mentioned; industry is also experimenting with its use in shale gas. There is some evidence that it can map the major fractures in a stimulated region. There may be some additional benefits in mapping changes in reservoir stress as production occurs. The use of 4D surveys for shale gas is in its early stages. Conclusions It can be seen from this review that the various geophysi-

Geophysics in reserves estim ation

cal techniques employed in the task of resource estimation for unconventional reservoirs are at a wide range of maturity and reliability. Surface 3D techniques for mapping the reservoir outline, discontinuities, and heterogeneous geobodies are well established and accepted. Other techniques such as microseismic are emerging technology. Unconventional resource plays are to a large extent the future of our industry. They are undergoing great expansion throughout the world. But the enormous engineering costs of these projects and their relatively narrow economic margins mean that it is more important than ever to have accurate and reliable estimates of recoverable resources early in the project life. Geophysics will play a large role in providing these data; it will be an interesting time for those of us in the field.
References
Berkhout, A. J., 1984, Seismic resolution: Geophysical Press. Dragoset, H. and Z. Jericevic, 1998, Some remarks on surface multiple attenuation: Geophysics, 63, no. 2, Supplement 772–789, doi: http:// dx.doi.org/ 10.1190/1.1444377. Geiser, P., A. Lacazette, and J. Vermilye, 2012, Beyond “dots in box”: an empirical view of reservoir permeability with tomographic fracture imaging: First Break, 30, July. Hampson, D. and L. Mewhort, 1983, Vertical seismic profile distinguishes multiples: Journal of the Canadian Society of Exploration Geophysicists, 19, 16–33. Hendrick, J., S. Lovric, E. von Lunen, J. Leslie-Panek, S. Bowman, and T. Urbanic, 2012, Decimating a microseismic dataset: How many are needed events to properly sample hydraulic frac stages with a multiarray monitoring program? Geovison-2012 CSPG CSEG CWLS Convention, poster presentation. Kelamis, P. G. and D. J. Vershuur, 2000, Surface related multiple elimination on land seismic data—Strategies via case studies: Geophysics, 65, no. 3, 719–734, http://dx.doi.org/10.1190/1.1444771. Mayerhoffer, M. J., E. P. Lolon, J. E. Youngblood, and J. R. Heinze, 2006, Integration of microseismic fracture mapping results with numerical fracture network production modeling in the Barnett Shale: SPE paper 102103. Maxwell, S., 2011, What does microseismic tell us about hydraulic fractures?: CSPG CSEG CWLS Convention. Rodriguez, V., 2012, Stochastic simulation of events not recovered from monitor records: presented at Canadian Geo-Convention 2012. Schlumberger Oilfield Glossary, http://www.glossary.oilfield.slb.com/ Display.cfm?Term=vertical%20seismic%20profile. Tsvankin, I., J. Gaiser, V. Grechka, M. van der Baan, and L. Thomsen, 2010, Seismic anisotropy in exploration and reservoir characterization: An overview: Geophysics, 75, 75A15–75A29, doi: http://dx.doi. org/ 10.1190/1.3481775. Vasudevan, K., D. W. Eaton, and F. Forouideh, 2011, Spatio-temporal complexity of microseismic events in a hydraulic fracturing experiment- a graph theory approach: Canadian Geo-Convention. Vermeer, G. J. O., 1990, Seismic wavefield sampling: A wavenumber approach to acquisition fundamentals: SEG, http://dx.doi. org/10.1190/1.9781560802440. Vermilye, J. M., and C. H. Scholz, 1998, The process zone: A microstructural view of fault growth: Journal of Geophysical Research. Solid Earth, 103, 2223–2237. Verschuur, E., 1991, Surface related multiple elimination, an inversion approach: Ph.D. thesis, Delft University.

Yilmaz, O., 2001, Seismic data analysis: SEG, http://dx.doi. org/10.1190/1.9781560801580.

Acknowledgments: We thank Nexen and the various contributors, especially Marian Hanna, who helped us with the preparation of this article. Their comments lead us to believe that this article can form the basis for further discussion of geophysical strategies for unconventional reserves and resource estimation. Corresponding author: [email protected]

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Sections/Associated Societies Focus
German Geophysical Society affiliates with SEG
he SEG Executive Committee, at its meeting in May, approved a petition by the Deutsche Geophysikalische Gesellschaft e.V. (DGG; German Geo-physical Society) to become an SEG Section/ Associated Society. The first joint activity between the societies will be the Nearsurface Honorary Lecture by Rick Miller on 1 October 2012 in Berlin followed by one on 3 October 2012 in Hannover. DGG is actually eight years older than SEG. It was founded in 1922 by a group of geophysicists associated with famed seismologist Emil Wiechert, and is the principal professional society of the geophysical community in Germany. DGG currently has 1100 active members in 34 countries; this includes students and professionals as well as individual and corporate memberships. The annual general assembly showcases recent research and provides young researchers the opportunity to present their work. DGG celebrated its 90th anniversary in April during the general assembly in Hamburg, where the main scientific topics were geophysical Earth system research, passive seismics in applied geophysics, and natural hazards. This traditional colloquium day dealt with the topic “Applied rock physics.” The day after the assembly offers a joint workshop

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(with, e.g., SEG or EAGE) that attracts participants from Europe and overseas. Recent topics included CO2 storage and geothermics; this year’s focus was “Geophysics for unconventionals.” DGG’s most visible and highly respected product is Geophysical Journal International. This ISI-indexed journal, published monthly as a joint effort with the Royal Astronomical Society, provides research articles in all subdisciplines of geophysics as well as interdisciplinary geosciences studies. For communication with its members, DGG also publishes three issues per year of its Red Booklets. This is the medium used by DGG’s seven workgroups to report about topical seminars and activities. The 2013 general assembly will be in Leipzig, DGG’s “native” city. Tomography, engineering, and environmental geophysics, as well as history of geophysics are the probable main topics. —Charlotte Krawczyk Managing Board Member and Cooperations Officer of Deutsche Geophysikalische Gesellschaft

Part of the exhibition at the DGG general assembly in Hamburg 2012, with discussion forum in the back.
1098 The Leading Edge September 2012

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Student zone
Serbian Student Chapters working together: The 3rd International Geosciences Student Conference in Belgrade, Serbia

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n Serbia we like to say, “The third one is the lucky one!” And this is why we were so optimistic about The 3rd SEG/EAGE/ AAPG International Geosciences Student Conference (IGSC) held in Belgrade from 29 May to 1 June 2012. In 2010, the SEG Student Chapter in Bucharest, Romania broke the ice with the first IGSC. The SEG Student Chapter in Krakow, Poland sponsored the second in 2011. In Belgrade in 2012, for the first time, the IGSC was organized by two student chapters: Faculty of Mining and Geology, University of Belgrade Student chapter and Faculty of Ecology and Environmental Protection, Union University Nikola Tesla Student Chapter, under the supervision of the Association of Geophysicists and Environmentalists of Serbia (AGES). IGSC coordinator Sasa Smiljanic stated, “Being part of the organization team of the 3IGSC was a heavy and responsible task. I must say it was not easy at all, but it was all worth it. It was exciting and truly inspiring! The high level of international participation in our conference was inspirational and pointed out new concepts and opportunities for many of us. I feel that only now do I fully appreciate the student geosciences network that we created together with our student colleagues from various universities and professional societies—SEG, AAPG, and EAGE, and also our Serbian professional association, AGES.” Before the conference kicked off, the SEG/ExxonMobil Student Education program brought together 27 students from 11 universities in five countries to focus for 2½ days on preparing for the breadth and challenges of an oil industry career. The Student Education Program puts PhD and graduate students in a classroom setting with ExxonMobil instructors. “Based on what I learned in the SEP program, I am interested in developing myself in the geosciences. Rapid and successful activity in the explo-

ration and production of oil and gas can be achieved only with the aid of a specialized force of skilled scientists and engineers. The SEP course provides skills for interdisciplinary collaboration for our future career development,” said Daria Deshenenkova from Gubkin Russian State University of Oil and Gas. The conference started with two full days of technical presentations. Instructors included Nick Riley (CO2GeoNet/CGS Europe), Koya Suto (Australian SEG President-Elect), Dan Herold (executive vice president, Parallel Geoscience Corporation), Nenad Grubin and Ana Dasović (Rio Tinto), Jelena Manić (Belgrade Open School, Centre for Career Guidance), Milos Ivačković (Mountain Rescue Service of Serbia), SEG 2012 Europe Honorary Lecturer Ian Jones, István Vető (EAGE Student Lectures Tour), and Alla Shogenova (ENeRG). Attendance at the 3rd IGSC included 244 students from 46 different universities. The technical program consisted of 94 papers presented in several sessions: physics of the Earth’s interior, near-surface geophysics, geo-energy, medical geology and other issues related to the environment, climate changes and energy, as well as nonmainstream topics such as geobiology, geoheritage, and geoethics. The exhibition floor offered an opportunity for students to meet representatives from international companies, energy companies, publishers, and supporting societies (such as SEG). After several days of concentrated networking and learning, the students were able to unwind at the SEG Serbian Challenge Bowl and the closing Gala event. The Challenge Bowl brought 12 teams together to compete in an intense battle testing their knowledge of the geosciences. Aurelian Roeser and Laura Stutenbecker from Freie Unversität Berlin were the winners and will compete in Challenge Bowl Finals at the SEG 2012 Annual Meeting in November. A first for an IGSC conference was a forum aimed at connecting previous and future IGSC organizers to share their experiences and ensure sustainable growth of the event with the mentoring of student chapters. “Everything was great and my traveling to Serbia and participation in the IGSC Forum gave

SEP participants take a break during the SEP in downtown Belgrade. Participants include Anatolii Kostrets,Taras Shevchenko University of Kyiv; Natalya Kalyniy, Ivano-Frankivsk National Technical University of Oil and Gas; Nataliia Troinich, Taras Shevchenko National University of Kyiv; Gumru Muradova, Baku State University; Rahman Mustafayev, Azerbaijan State Oil Academy; Vasiliy Korobkin, Gubkin Russian State University of Oil and Gas; and Anna Skorkina, Perm State National Research University.
1100 The Leading Edge September 2012

IGSC students taking a field trip in Mećavnik, Serbia on the last day of the conference.

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2012 SEP Europe, Belgrade, Serbia.

Students at the SEP in Belgrade, Serbia: Daria Daudina, Lomonosov Moscow State University and Sultan Safin, Kazan Federal University.

Student posters at IGSC. From left to right: Leyla Shikhova, Baku State University; Vadym Antoshchuk, Taras Shevchenko University of Kiev; Dmytro Petrovskyy, Ivano-Frankivsk National Technical University of Oil and Gas; Daria Deshenenkova, Gubkin Russian State University of Oil and Gas; Bogdan Shyrkov, Taras Shevchenko National University; Nataliia Troinich, Taras Shevchenko National University of Kyiv; and Gumru Muradova, Baku State University.

me motivation for the future. During the conference, we met students from Poland, Romania, Germany, and Serbia who taught me how I can be a leader and how we can organize such conferences in the future,” said Gumru Muradova from Baku State University. SEG has committed to supporting three IGSCs in 2013 (in Medellin, Colombia; Berlin, Germany; and Lagos, Nigeria) with organizers from SEG Student Chapters. The last day was dedicated to a field trip through southwest Serbia’s Zlatibor mountain region, where students conducted geological research of old geologic formations and enjoyed beautiful sightseeing. Students also enjoyed visiting and taking pho-

tos in the famous wooden town in Mećavnik, 200 km southwest of Belgrade. I am proud to have organized Geo Day, a program promoting geosciences to students in Serbia from kindergarten through high school. The students completed posters on the topic “How to save planet Earth?” The three best posters from each age level were given awards by AGES. Furthermore, participants listened to lectures about how challenging and exciting a career in geosciences can be. The 3rd IGSC was indeed a lucky one! We had a wonderful time during the conference; all students found something of interest to advance their knowledge. It was nice to see many old friends and make a lot of new friends and professional ties. The Serbia chapters are happy to have hosted the 3rd IGSC and to witness how they are connecting people in life-long relationships, which will ensure a bright future for the geosciences worldwide. The fact that I was in the position to organize a student event of this magnitude is beautiful! I hope to be present at many more IGSCs in the future and I personally encourage all students to consider attending one of the 2013 IGSCs.
Acknowledgments: Special thanks to the entire 3rd IGSC organizing team for its hard work and dedication. Also, special thank you to Sultan Safin, Kazan Federal University.

—Marko Vanić, University of Belgrade Event Coordinator, Serbia IGSC

“The International Geosciences Student Conference in Belgrade was the first international event I’ve visited where so many young, smart, and talented people participated not because they had to but because they wanted to. It made my goals much higher than before the conference. Now I see a new way to go and new challenges to overcome. Everyone was amazing! It was great to meet everyone in one place, to speak with them, to walk with them … and to continue all these conversations after the conference. This conference was just the beginning. I am sure everyone took something from the conference, based on what I read in every message I’ve received from my new friends. I really don’t know what the best moment was—I can answer only that the conference moment was the best.”

—Anna Skorkina Perm State National Research University
1102 The Leading Edge September 2012

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Reviews
Seismic Imaging and Inversion: Application of Linear Inverse Theory, by R. H. Stolt and A. B. Weglein, ISBN 978-1-107-01490-9, Cambridge University Press, 2012, 404 pp., US $125. This book, the first of a proposed two-volume series, presents the basic concepts and relationships between wave-equation migration and inversion technologies. Migration or seismic imaging techniques are concerned with the determination and restoration of the proper geometric locations of reflections caused by spatial variations in rock properties. Inversion methods quantify the magnitude and sign changes of those same rock properties from seismic data. Migration and inversion technologies play a central role in exploration geophysics as both are required to accurately determine the location and quantity of hydrocarbons in the subsurface. The prevailing view of imaging and inversion technologies is that they are separate and unrelated. A key contribution of this new volume is to demonstrate the relationship that exists between the two through inverse scattering theory. In this first volume, the focus is on migration algorithms that map recorded seismic data into seismic images and inversion methodologies that transfer the seismic images into rock properties. The physics of seismic imaging and inversion requires complex and largely nonlinear equations to describe the dynamics of the processes. Linearization of these equations into simpler forms is routinely performed to implement solutions as computer processing modules. The primary motivation for linearization is driven by practical constraints of computer speed and memory. The inevitable, negative consequence of linearization is a reduction in accuracy of the computer processing results. Today, most leading-edge seismic processing algorithms incorporate some degree of linearity in their implementations. The second volume of this series will move forward into the more complex nonlinear methods that will overcome the current limitations of processing software. Volume 1 begins with an overview of modeling, migration, imaging and inversion, and the interrelationships between these technologies. This sets the foundation for subsequent chapters discussing linear inverse scattering theory that puts seismic imaging and inversion on a single, equal footing. This development allows the merging of the two fields because each is now based on a common inverse scattering theory. The authors introduce the concept of scattering potential from which the seismic reflectivity function can be derived. Viewing the scattering potential as the generator of seismic reflection data allows imaging of surfaces and diffractors without the need for interpreter intervention or separate imaging methods/models. Each chapter in the book ends with a set of exercises for the reader that challenges them to apply the concepts covered in that chapter to a practical problem. This makes the volume suitable for use as a textbook for a graduate-level geophysics course. The authors of this series, each having large and long-term bodies of creative research in these technologies, are well known and respected by the worldwide geophysics community. I believe this series represents an important contribution to geophysical literature. The novel methods and conceptualization of seismic imaging and inversion methodologies will significantly impact research and development in our industry for decades. —Michael McCormack Sequim, USA

Fundamentals of Geophysical Interpretation
Laurence R. Lines and Rachel T. Newrick Fundamentals of Geophysical Interpretation, SEG Geophysical Monograph Series No. 13, is a practical handbook for the petroleum geophysicist. Fundamental concepts are explained using heuristic descriptions of seismic modeling, deconvolution, depth migration, and tomography. Pitfalls in processing and contouring are described briefly. Applications include petroleum exploration of carbonate reefs, salt intrusions, and overthrust faults. The book includes past, present, and possible future developments in time-lapse seismology, borehole geophysics, multicomponent seismology, and integrated reservoir characterization. ISBN 978-1-56080-125-2 Catalog #153A Published 2004, 288 pages, Paper SEG Members $29, List $39, E-book $39

Order publications online at: www.seg.org/publications or E-mail: [email protected]
1104 The Leading Edge September 2012

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SEG-AGU Joint Workshop Cryosphere Geophysics: Understanding a Changing Climate with Subsurface Imaging
6-8 January 2013 Boise State University, Boise, Idaho, U.S.A.

Abstract submission deadline: 20 September 2012 Advance registration deadline: 29 November 2012
The earth’s cold regions present perhaps the most diverse set of geophysical problems of any earth system. We must understand the influence of water in all its phases on the dynamics and thermodynamics of snow, ice and frozen soil masses whose geophysical properties can change dramatically on time scales from hours to millennia. This workshop will exchange concepts and ideas on the development and application of geophysical exploration methods to problems in the changing Cryosphere relating to snow, sea ice, permafrost, glaciers and ice sheets. We will focus mainly on how various methods of subsurface imaging can help monitor changes in the Cryosphere and thus elucidate the consequences of a changing climate. These changes may include the mass balance of ice sheets and glaciers, active layer depth and extent, the state and depth of terrestrial and offshore permafrost, and the mass budget and state of sea ice and the seasonal snow cover. The workshop will also highlight advances in geophysical methods, especially as may be relevant to resource development, environmental hazard monitoring and assessment, and bridging the gap between development and practical application of geophysical technology. We invite papers that investigate all aspects of cold regions subsurface imaging and extraction of in situ petrophysical properties. Contributions may include:

1. Case histories of the use of seismic, electrical and electromagnetic, gravity, and magnetic methods in the Cryosphere. 2. Applications of remote sensing methods to aid interpretation of subsurface images, such as airborne and satellite observations. 3. Advances in established methods and new approaches for subsurface imaging. 4. Advances in established methods and new approaches for estimation of material properties from subsurface images as well as from in situ and ground truth petrophysical data sets. 5. Operational advances in extreme environments and over more challenging temporal and spatial resolutions and scales.
Authors should prepare a 4 page extended abstract and plan to give an oral presentation of their paper. In addition, there will be an interactive session designed to provide participants feedback on problem datasets or problematic interpretations. Authors participating in this session will have the opportunity to upload data for analysis and review by other participants prior to the workshop. Additional poster sessions may be added depending on the number of paper submissions.

Meeting Schedule:
Sunday, 6 January – Registration and Ice Breaker Monday, 7 January through Tuesday, 8 January – Sessions will be held Monday and Tuesday mornings and afternoons. The workshop will include a dinner on Monday evening. Abstract Format: Abstracts should include sufficient detail for the committee to judge the quality of the proposed presentation. Abstracts should be a 4 page extended abstract, in Times Roman font size 10-12 points. The title should be in bold font. Below the title, authors should be listed. Immediately below the list of authors, please give the affiliations and email addresses for all authors. All text must stay 1 inch clear of the margins of the page. Meeting Venue: Boise State University will serve as the meeting venue. A block of hotel rooms has been booked at The Grove Hotel in Boise. Abstract Submission: All abstracts along with application forms must be submitted electronically to [email protected] in PDF format, by 20 September 2012. Workshop Participants: In addition to those submitting abstracts for presentation, we welcome at this time application forms from others interested in attending the workshop. If the number of applicants exceeds the capacity of the conference facilities, preference will be given to presenters. Capacity for this workshop is 100 participants. Registration: All registration rates will be published when the acceptance letters for presentations are sent out. Student Registration: Students are strongly encouraged to submit abstracts. We anticipate having funding available to subsidize registration costs for students. Preference will be given to students who participate with a presentation. Sponsorship: Sponsorship opportunities are available by contacting Amy Watson at [email protected] or at http://www.seg.org/meetings/cryo13.

John Bradford, Chairman (Boise State University), Steven Arcone (Cold Regions Research and Engineering Lab), Hajo Eicken (University of Alaska Fairbanks), Hans-Peter Marshall (Boise State University)

Organizing Committee

www.seg.org/meetings/cryo13

Full ProFessor / Chair oF aPPlied GeoPhysiCs
The Montanuniversitaet Leoben, Austria, invites applications for its Chair of Applied Geophysics; start date: autumn 2013. This full-time tenured faculty position comes with secretarial support and additional senior researcher and technician positions to be filled by the successful applicant. The chair is associated with the Department of Applied Geosciences and Geophysics (DAGG) and affiliated with a research center studying magnetism and a group with a focus on petrophysics. The DAGG is part of an alliance of Styrian universities in the field of applied geosciences (UZAG). Qualified are researchers of international repute and experience in the field of seismic processing and interpretation / reservoir geophysics as document by a track record in academic and industry-funded research projects including field studies and / or JIPs. The willingness to engage in multidisciplinary research projects together with colleagues from Petroleum and Subsurface Engineering, the UZAG, and stakeholders in the private and public sector is essential. Examples of appropriate research areas include, but are not limited to: reservoir characterization, modeling and monitoring; seismic processing and attribute analysis including algorithm development; coupled inversion of seismic and other geophysical data; non-conventional hydrocarbon resources; and borehole geophysics. The Chair of Applied Geophysics contributes substantially to the design and teaching of the BSc/MSc programs in Applied Geosciences, Petroleum Engineering, and Resource Engineering. Several courses are taught in English, but the knowledge of the German language is an advantage. Prerequisites for an appointment at the full professor level are a PhD in the field of research, and a habilitation or equivalent demonstration of the ability to teach, supervise graduate students, attract research funding, and successfully conduct research projects. The salary depends on the level of experience with a minimum of 4571 € monthly for full-time employment at the A1 job-level classification of the Austrian university law. It is negotiable. The Montanuniversitaet Leoben is striving to increase the representation of women in its faculty. Therefore, we explicitly encourage qualified female researchers to apply and stress that – given equivalent skills – their recruitment takes precedence. Applications should include a curriculum vitae, description of research interests and vision with reference to the research profile of the Montanuniversitaet (http://www.unileoben.ac.at), as well as the 5 most important publications; all supplied via CD ROM, 5 copies. They should be directed to the Rector of the Montanuniversitaet Leoben Franz-Josef-Strasse 18 8700 Leoben, Austria Application deadline is 31/10/2012. For more information, please contact the head of the search committee: Prof. R.F. Sachsenhofer Tel.: +43 3842 402 6300 E-mail: [email protected] The Rector of the Montanuniversitaet Leoben Univ.-Prof. Dipl.-Ing. Dr. Wilfried Eichlseder

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Announcements
2012–2013 SEG Board of Directors The following SEG members have been elected to serve on the 2012–2013 Board of Directors: Don Steeples, presidentelect; Dennis Cooke, second vice president; Gary Servos, treasurer; and Peter Annan, Elsa Jaimes, Alfred Liaw, Samir Abdelmoaty, Edith Miller, and Christine Krohn, directors at large. The 14-member Board will be led by David Monk, the current President-elect. Completing the 2012–2013 Board will be Richard Miller, the current Second Vice President who will become First Vice President; Tamas Nemeth, who will be in the final year of his two-year term as Editor; and Bob Hardage, the current President who will take the newly established position of Past President. The final position on the Board will be the Chair of the Council and it will be filled by a vote of the members of the Council in September. New District Representatives The following SEG members have been elected to serve as District Representatives: Greg Partyka and William Brumbaugh, District 2; Randy Keller, District 4; John Eastwood, District 5; Bernard Verdu, District 6; Olav Inge Barkved and Karl Berteussen, District 7; Erik Verschuur, District 8; Carlos Planchart and Philip Fontana, District 10; and Sam Zandong Sun, District 11. KEGS Foundation announces 2012 scholarships The KEGS Foundation has awarded a record 24 scholarships totaling nearly C $16,000 to 14 undergraduate and ten graduate students in geophysics at 13 Canadian universities for the 2012–13 academic year. The increased number and level of scholarships is in response to a record number of applicants, aided by the continued strong financial support of the Foundation by the geophysical and exploration community across Canada, in turn fostered by the continuing high level of activity in the resource sector. The undergraduate recipients of KEGS Foundation scholarships for 2012–13 are Emily Tess Baker and Hakim Shukri (University of British Columbia); Jeremy Gosselin (University of Victoria); Hoai Nam Vien (University of Saskatchewan); Tim Hayward (University of Manitoba); Frank Joris (University of Western Ontario); Kun Guo (University of Toronto); Jessica Steeves and William Smith (Queen’s University); Dong Shi and Phil Van-Lane (University of Waterloo); Jade Ghaoui (Université Laval); Colin Brisco and Chad Gidge (Memorial University). The ten graduate recipients are Andrew Ringeri (University of New Brunswick); Michal Kolaj (Laurentian University); Ble Jean Fidele Yrro (École Polytechnique); Gabriel Fabien-Ouellet (Université Laval); Laura Quigley (University of Toronto); Samira Alipour, Marie Burford, Attieh Eshaghi, Innocent Ezenwa; and Hadi Ghofrani (University of Western Ontario). Scholarships range from $500 to $1000, depending on the recipient’s merit and need and the available Foundation funds.

Edge and Tip Diffractions: Theory and Applications in Seismic Prospecting
Kamill Klem-Musatov, Arkady Aizenberg, Jan Pajchel, and Hans B. Helle
In Edge and Tip Diffractions: Theory and Applications in Seismic Prospecting (SEG Geophysical Monograph Series No. 14), the theoretical framework of the edge and tip wave theory of diffractions has been elaborated from fundamental wave mechanics. Seismic diffractions are inevitable parts of the recorded wavefield scattered from complex structural settings and thus carry back to the surface information that can be exploited to enhance the resolution of details in the underground. The edge and tip wave theory of diffractions provides a physically sound and mathematically consistent method of computing diffraction phenomena in realistic geologic models. In this book, theoretical derivations are followed by their numerical implementation and application to real exploration problems. The book was written initially as lecture notes for an internal course in diffraction modeling at Norsk Hydro Research Center, Bergen, Norway, and later was used for a graduate course at Novosibirsk State University in Russia. The material is drawn from several previous publications and from unpublished technical reports. Edge and Tip Diffractions will be of interest to geoscientists, engineers, and students at graduate and Ph.D. levels.

ISBN 978-1-56080-149-8, Published 2008, 201 pages, Paper Catalog #154A, SEG Members $79, List $99, e-book $99

To order publicaitons: E-mail: [email protected] Visit: www.seg.org/publications
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September 2012

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The Leading Edge

Calendar
2012 SEG Continuing Education Course: Understanding Seismic Anisotropy in Exploration and Exploitation: Hands On, Houston, Texas, USA, www.seg.org/ce, ([email protected]) 10–11 Sep SEG Continuing Education Course: Magnetotellurics for Natural Resources: from Acquisition through Interpretation, Houston, Texas, USA, www.seg.org/ce, (ce@ seg.org) 10–11 Sep SEG/SBGf/SPE Joint Workshop: Global Perspectives for Deepwater Presalt Exploration and Development, Rio de Janeiro, Brazil, www.seg.org/meetings/rio2012, ([email protected]) 11–12 Sep SEG Continuing Education Course: Marine Electromagnetic Methods for Hydrocarbon Exploration, Houston, Texas, USA, www. seg.org/ce, ([email protected]) 12–13 Sep SPE/SEG Workshop “Injection Induced Seismicity” Broomfield, Colorado, USA, http://www.spe.org/ events/12aden/ 12–14 Sep SEG/ExxonMobil Student Education Program, Istanbul, Turkey, www.seg.org/students/SEPExxon, ([email protected]) 14–16 Sep 15th Annual AAPG/SEG Fall Expo, Houston, Texas, USA, www.studentexpo.info 17–18 Sep Istanbul International Geophysical Conference and Oil and Gas Exposition, Istanbul, Turkey, www. icgistanbul.com 17–19 Sep SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena, Villahermosa, Mexico, www.seg.org/disc, (disc@ seg.org) 18 Sep KSEG International Symposium on Geophysics for Discovery and Exploration, Jeju City, Republic of Korea, http://2012symp.seg.or.kr 19–21 Sep SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena, Caracas, Venezuela, www.seg.org/disc, ([email protected]) 21 Sep VIII Azerbaijan International Geophysical Conference: Integrated Approach for Unlocking Hydrocarbon Reserves, Baku, Azerbaijan, http://www.aapg.org/baku2012/, ([email protected]) 3–5 Oct Society of Petroleum Engineers Annual Meeting, San Antonio, Texas, USA, http://www.spe.org/ index.php 8–10 Oct SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena, Las Vegas, Nevada, USA, www.seg.org/disc, (disc@seg. org) 2 Nov SEG/ExxonMobil Student Education Program, Las Vegas, Nevada, USA, www.seg.org/students/SEPExxon ([email protected]) 2–4 Nov SEG/Chevron Student Leadership Symposium, Las Vegas, Nevada, USA, www.seg.org/students/ SLSChevron, ([email protected]) 3–4 Nov SEG Continuing Education Courses, Las Vegas, Nevada, USA, www. seg.org/ce, ([email protected]) 3–4 Nov Geological Society of America Annual Meeting and Exposition, Charlotte, North Carolina, USA, http://www.geosociety.org/meetings/2012/ 4–7 Nov SEG International Exposition and 82nd Annual Meeting, Las Vegas, Nevada, USA, www.seg.org/am 4–9 Nov Arctic Technology Conference, Houston, Texas, USA, www.arctictechnologyconference.com 3–5 Dec SEG/KOC Joint Workshop: Global Single Sensor Acquisition and Processing—Past, Present and Future, Kuwait City, Kuwait, www.seg.org/meetings/kuwait12, ([email protected]) 3–6 Dec AGU Fall Meeting 2012, San Francisco, California, USA, http://www. agu.org/meetings/ 3–7 Dec SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenonmena, Mumbai, India, www.seg.org/disc, ([email protected]) 7 Dec SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena, Abu Dhabi, United Arab Emirates, www.seg.org/disc, ([email protected]) 10 Dec SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena, Muscat, Oman, www.seg.org/disc, ([email protected]) 12 Dec

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The Leading Edge

September 2012

SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena, Al Khobar, Saudi Arabia, www.seg.org/disc, (disc@seg. org) 16 Dec SEG DISC: Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenonmena, Ankara, Turkey, www.seg.org/disc, ([email protected]) 18 Dec 2013 SEG/AGU Joint Workshop: Cryosphere Geophysics: Understanding a Changing Climate with Subsurface Imaging, Boise, Idaho USA, www.seg.org/meetings/cryo13, ([email protected]) 6–8 Jan

SEG Microseismic Technology Forum: Evaluating Monitoring Techniques: Downhole, Buried and Surface, Napa, California, USA, ([email protected]) 22–24 Jan SPE/SEG Joint Applied Technology Workshop: Giant Fields Monitoring—Are We Doing It Right?, Dubai, UAE, www.spe.org/ events/12adu6 12–13 Feb SAGEEP 2013—Symposium on the Application of Geophysics to Engineering and Environmental Problems, Denver, Colorado, USA, ([email protected]) 17–21 Mar International Petroleum Technology Conference, Beijing, China, http:// www.iptcnet.org/2012/index.php, ([email protected]) 26–28 Mar

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Active Faults of the World
Robert Yeats
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Continuum Mechanics in the Earth Sciences
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September 2012 The Leading Edge 1111

Magnetotellurics in the Context of the Theory of Ill-Posed Problems
Mark N. Berdichevsky and Vladimir I. Dmitriev

This volume serves as an introduction to modern magnetotellurics originating with the pioneering work of Tikhonov and Cagniard. It presents a comprehensive summary of theoretical and methodological aspects of magnetotellurics. It provides a bridge between textbooks on electrical prospecting and numerous papers on magnetotelluric methods scattered among various geophysical journals and collections. The book has been written in the terms of the theory of ill-posed problems and contains a special chapter encouraging readers to master the elements of this theory that defines the philosophy of the physical experiment. The book thus offers the connected and consistent account of the principles of magnetotellurics from that single viewpoint. The book also brings together developments from many sources and involves some little-known results developed in Russia in Tikhonov’s magnetotellurics school. Of particular interest are concluding chapters of the book that demonstrate the potential of magnetotellurics in oil and gas surveys, including discovery of the Urengoy gas field in Western Siberia, one of the largest gas fields in the world. This potential also is revealed in studies of the earth’s crust and upper mantle. ISBN 978-1-56080-106-1, 2002, 215 pages, Hardcover, Catalog #113A SEG Members $36, List $45

Order publications online at: www.seg.org/publications or E-mail: [email protected]

SEG GEophySical DEvElopmEntS SEriES no. 12

Seismic True-Amplitude Imaging
Jörg Schleicher, Martin Tygel, and Peter Hubral A rich literature exists on computational methods based on wave equations for seismic imaging and earth‑parameter estimation. Somewhat lost in the advance to progressively more sophisticated computational techniques are the intuitive ideas with roots that reach back to Hagedoorn and are based on ray theory, the geometry of data, and the geometry of wave propagation. In Seismic TrueAmplitude Imaging (SEG Geophysical Developments Series No. 12), the authors describe their research of many years, demonstrating that those simple ideas also lead to a broad description of the structure of the earth’s interior and the changes in medium parameters across reflectors. Demonstrations in the open literature of the efficacy of their methods abound. Now those ideas have been collected and reorganized. The book gives a pictorial presentation of the basic principles of Kirchhoff‑type imaging and proceeds to a comprehensive treatment of its kinematic and dynamic aspects. The text is a valuable addition to the library of anyone interested in the theory and practices of seismic data processing for imaging and parameter estimation with all its attendant processes. ISBN 978-1-56080-143-6 Catalog #133A Published 2007, 350 pages, Hardcover SEG Members $79, List $99, E-book $99

To order publications: E-mail: [email protected] Visit: www.seg.org/publications
1112 The Leading Edge September 2012

Problems in Exploration Seismology and their Solutions
Lloyd P. Geldart and Robert E. Sheriff

This book is designed for students and for geophysicists who have forgotten the basic theory required to solve practical problems. Geophysical texts often provide problems, but this book is unique in that it provides solutions also. The authors give a summary of the basic theory required to solve each problem. The 212 problems cover a wide range, including least-squares methods, choosing velocities for various situations, z-transforms, determining 2D and 3D field geometries, and solving processing and interpretation problems. ISBN 978-1-56080-115-3 Catalog #174A Published 2004, 524 pages, Hardcover SEG Members $49, List $64, E-book $64

To order publications: E-mail: [email protected] Visit: www.seg.org/publications

Seismology of Azimuthally Anisotropic Media and Seismic Fracture Characterization
Ilya Tsvankin and Vladimir Grechka

Because most sedimentary rocks encountered in oil and gas exploration are effectively anisotropic, it is imperative to properly estimate seismic anisotropy and incorporate it into data-processing and imaging algorithms. Seismology of Azimuthally Anisotropic Media and Seismic Fracture Characterization (SEG Geophysical References Series No. 17) presents a systematic analysis of seismic signatures for azimuthally anisotropic media and describes anisotropic inversion/processing methods for wide-azimuth reflection data and vertical seismic profiling (VSP) surveys. The main focus is on kinematic parameter-estimation techniques operating with P-waves as well as with the combination of PP and PS (mode-converted) data. The part devoted to prestack amplitudes includes azimuthal amplitude variation with offset (AVO) analysis and a concise treatment of attenuation coefficients, which are highly sensitive to the presence of anisotOrder publications online at: ropy. Discussion of fracture characterization is based on modern effective-media theories and illustrates www.seg.org/publications or both the potential and limitations of seismic methods. Field data examples highlight the improvements E-mail: [email protected] achieved by accounting for anisotropy in seismic processing, imaging, and fracture detection. ISBN 978-1-56080-228-0 Catalog #177A Published 2011, 480 pages, Hardcover SEG Members $79, List $99, e-book $99

September 2012

The Leading Edge

1113

The Leading Edge

Membership
Applications for Active membership have been received from the candidates listed below. This publication does not constitute election but places the names before the membership at large in accordance with SEG’s Bylaws, Article III, Section 5. If any member has information bearing on the qualifications of these candidates, it should be sent to the president within 30 days. The list can be viewed online at membership.seg.org/applicants/. For Active membership Baker, Austin (Baker Hughes Inc, Houston, USA) Boily, Michel (GEON, Montreal, Canada) Brown, Joy (Taqa North Ltd., Calgary, Canada) Clayton, Robert (California Institute of Technology, Pasadena, USA) Ellis, Kristian (Polarcus DMCC, Pendlebury, Manchester, UK) Ellis, Michelle (RSI, Houston, USA) Jegede, Tope (Shell, Portt Harcourt, Nigeria) Johnston, Matthew (Self-employed, Timmins, Canada) Lankford, Thomas (Global Geophysical Services, Houston, USA) Lawal, Taofiq (Conoil Producing Limited, Lagos, Nigeria) Mi, Greg (Husky Energy China Oil Ltd., Canada) Slind, Christopher (Downunder Geosolutions, Houston, USA) Requirements for Membership Active: Eight years professional experience, partly involving exercise of independent judgment. Membership applications and details on other types of membership, including Associate, Student, and Corporate, may be obtained at http://membership.seg.org.

Smith, David (Oxy, Houston, USA) Vartan, Alex (PGS Americas Inc, Houston, USA) Zhu, Weihong (Saudi Aramco, Dhahran, Saudi Arabia) For transfer to Active membership Aboshihata, Ayman (Maersk Oil, Houston, USA) Dunsmore, Dennis (Vermilion Energy Trust, Canada) Haack, Josh (Halliburton, Houma, USA) Sirtautas, Anthony (Saudi Aramco, Dhahran, Saudi Arabia) For reinstate and transfer to Active membership Lizarraga Ruiz, Francisco (Western Geco, Alvaro Obregon, Mexico) Ramirez, Adriana Citlali (Statoil, Trondheim, Norway)

Wish you could take Technical Program presentations home with you?

Now you can.

Preorder your copy of the 2012 Selected Technical Program Presentations DVD-ROM.
Approximately 200 technical program presentations will be recorded for inclusion on this DVDROM. View the slides used by the presenter while you listen to the audio recording — all from the convenience of your own computer. ➤ Order with your Annual Meeting registration or online at: www.seg.org/amregistration or individually at www.associationarchives.com/SEG
PRICING

Member (Domestic)............................................................US$125 Member (International) ........................................................US$135 Nonmember (Domestic) ....................................................US$165 Nonmember (International).................................................US$175
Ships 6-8 weeks after Annual Meeting Price includes shipping and handling.

Questions? Email: [email protected] • Phone: +1-918-497-5526
1114 The Leading Edge SelectedSessionsDVDad.indd 1 September 2012
8/21/12 8:21 AM

Attend the SEG workshop on geophysical input for resources evaluation.
Sponsored by the SEG Oil & Gas Reserves Committee, the workshop is titled Use of Seismic Technology in Petroleum Resources Estimation and Classification and will offer the opportunity to

❙ Discuss the geophysical input for resources evaluation as presented in PRMS Application Guidelines

❙ Hear updates and feedback about COGEH-CGF cooperation and SPE-PRMS specific workshops

Use of Seismic Technology in Petroleum Resources Estimation and Classification Thursday, 8 November 2012 1:30 pm – 5:00 pm SEG Annual Meeting, Las Vegas

❙ Hear oral presentations and participate in open discussion about articles in the current TLE special issue

SEG Professional Development
The following lecturers will be presenting at various locations around the world during the months of September and October 2012.
Distinguished Instructor Short Course Chris Liner

Elements of Seismic Dispersion: A Somewhat Practical Guide to Frequency-Dependent Phenomena
For complete course information, visit www.seg.org/disc.

➤ Book signing at SEG Annual Meeting Book Mart 11 a.m. to noon, Tuesday, 6 November
2012 Fall SEG-AAPG Distinguished Lecturer Manika Prasad

Shales and Impostors: Understanding Shales, Organics, and Self-Resourcing Rocks
For complete lecture information, visit www.seg.org/dl.
Supported by

2012 Near Surface Honorary Lecturer Rick Miller

Near-Surface Seismic: More than a Problem of Scale
For complete lecture information, visit www.seg.org/hl.

2012 Central & South America Honorary Lecturer Eduardo Filpo

Image Ray Time-To-Depth Conversion and Model Ray Applications
For complete lecture information, visit www.seg.org/hl. 2012 Middle East & Africa Honorary Lecturer Rocco Detomo, Jr.

4D Time-Lapse Seismic Reservoir Monitoring of African Reservoirs
For complete lecture information, visit www.seg.org/hl. 2012 North America Honorary Lecturer Shuki Ronen

Ocean-Bottom Acquisition and Processing: Past, Present, and Future
For complete lecture information, visit www.seg.org/hl.
Sponsored by Shell

SEG Annual Meeting Course Schedule
REGISTRATION IS OPEN!
Mandalay Bay Convention Center
4–9 November 2012 • Las Vegas, Nevada USA

SEG Distinguished Instructor Short Course (one day course)
Mandalay Bay Convention Center, Ballroom L Friday, 2 November • Check-in: 7 a.m.–8 a.m. • Course duration: 8 a.m.–5 p.m.
• Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena by Chris Liner

Continuing Education Courses (all courses are two days, unless otherwise noted) Mandalay Bay Convention Center, Breakers B-F & H-L and Lagoon A-L rooms Saturday–Sunday, 3-4 November 2012 • Check-in: 7 a.m.–8 a.m. • Course duration: 8 a.m.–5 p.m.
• 3D Seismic Attributes for Prospect Identification & Reservoir Characterization by Kurt Marfurt • 3D Seismic Data Acquisition: An Update on Modern Technologies and Usage Methodologies by Malcolm Lansley • A Practical Understanding of Inversion for Exploration Geophysics by John Bancroft • Application and Interpretation of Converted Waves by Robert Stewart and James Gaiser • Borehole Geophysics: Theory and Practice by Ron Hinds & Rick Kuzmiski • Concepts and Applications in 3D Seismic Imaging (one-day, 4 Nov.) by Biondo Biondi • Full Waveform Inversion by Mrinal Sen • Geophysical Applications of Time-Frequency Analysis by Marcilio Matos • Geophysics Role from Play, Prospect to the Well Head: Geopressure Perspective by Selim Shaker • Geophysics Under Stress: Geomechanical Applications of Seismic and Borehole Acoustic Waves by Colin Sayers • Gravity and Magnetics for Explorationists by Michal Ruder • Introduction to High Performance Computing by Jan Thorbecke • Petroleum Systems of Deepwater Settings by Paul Weimer • Planning and Operating a Land 3D Seismic Survey by Andreas Cordsen and Peter Eick • Processing, Inversion and Reconstruction of Seismic Data by Mauricio Sacchi • Seismic Anisotropy: Basic Theory and Applications in Exploration and Reservoir Characterization by Ilya Tsvankin and Vladimir Grechka • Seismic Data Interpretation in the Exploration Domain by Tim E. Smith • Seismic Imaging of Subsurface Geology (Acquisition, Processing, and Modeling) by Michael Schoenberger • Seismic Interferometry for Exploration and Production by Deyan Draganov and Kees Wapenaar • Seismic Stratigraphy and Seismic Geomorphology by Henry Posamentier  To view the full course descriptions and online registration visit www.seg.org/ce

REGISTER BY 25 October
After 25 October, only on-site registration available.

Register Online at www.seg.org/ce
For more information e-mail [email protected] or [email protected]

The Leading Edge

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ADlinc is offered free to display advertisers in the current issue of The Leading Edge. Submission of contact information is the responsibility of the advertiser.

Interpretation of Three-Dimensional Seismic Data, seventh edition
Alistair R. Brown
Interpretation of Three-Dimensional Seismic Data (SEG Investigations in Geophysics Series No. 9 and AAPG Memoir 42) is the definitive and now classic text on the subject. Conceived in 1979 and first published in 1986, the book helps geoscientists extract more information from their seismic data and improve the quality of their interpretations. The prime focus of the book continues to be the synergy between 3D seismic data and the workstation. The author passionately addresses the widespread problem of underuse of data. Two new chapters and several new sections have been added in the seventh edition, published in 2011, but basic data understanding continues to be stressed.

ISBN 978-0-89181-374-3 Catalog #114A

Published 2011, 646 pages, Hardcover with CD SEG Members $84, List $115

Order publications online at: www.seg.org/publications or E-mail: [email protected]
Interpret3DSeis_BrownQtrAd.indd 1 1118 The Leading Edge

September 2012

12/13/11 9:15 AM

SEG 2012 Honors & Awards Ceremony (NEW) Sunday, 4 November 4:30 p.m.
Be sure to attend the Honors & Awards Ceremony to recognize and to honor talented individuals and organizations that have advanced our science and benefited our Society.

Technical Luncheons Tuesday and Wednesday, 6-7 November
Don’t miss the Gravity and Magnetics luncheon with speaker Erik Scott and Richard Denne of Marathon Oil on Tuesday. The Development and Production Luncheon will feature speaker Eric von Lunen of Nexen on Wednesday. The Mining Luncheon will feature a prominent speaker and will also take place Wednesday.

• More than 600 oral & poster presentations • Over 8,000 industry professionals • More than 300 companies exhibiting • Cutting-edge sessions & workshops • Networking events

State of the Net

Opening the SEG Wiki toolbox
Chris Posey, Online Marketing Lead

I

n the March 2012 issue of The Leading Edge, we told you about the muchanticipated rollout of the SEG Wiki and the ways you can benefit from it: • The SEG Wiki is a simple, intuitive research tool • The SEG Wiki focuses exclusively on geosciences topics • The SEG Wiki is moderated by your peers • The SEG Wiki is populated by your peers … and Sheriff’s Encyclopedic Dictionary of Applied Geophysics, fourth edition Surely by now you’ve had the opportunity to experience these benefits firsthand. And while the SEG Wiki is still in its infancy, new terms, details, definitions, and comments are being added to the wiki all the time. Now that you’re familiar with how to use the wiki, perhaps you want to delve deeper and learn more about the changes people make to the wiki. You can do so with ease by “tracking changes,” by using the wiki “Watch” tool, and by viewing an individual article’s history.

The information following these abbreviations includes a timestamp and the username of the person who made the listed changes. Clicking on the contributor’s username takes you to his or her public user page. Here, you can view information provided by the user about the user. You will also notice two additional linked notations: Talk and contribs. By clicking on the Talk link, you will be able to view and contribute to the user’s “talk page.” A talk page is a page which can be used for discussion and communicating with other users. The contribs link leads you to a page that shows all wiki contributions of the user in question. Watch/Unwatch While the ability to view all of the changes made across all pages of the SEG Wiki is indeed a robust and handy capability, you may wish to limit your change-tracking exclusively to the Alaska 2D land line entry you were looking at in the first place. For this, consider using the Watch feature. Similar to the Recent changes display, the watchlist is a tool for tracking changes over time; however, it is limited to a user-defined set of “watched” pages. To watch a page, first access the page, then, select “Watch” from the drop-down menu at the top of the Web page. (You may choose to “Unwatch” an entry in the same manner.) Once selected, you may view changes to the entry simply by accessing your watchlist. To view your watchlist, again select “Watch” from the drop-down menu at the top of the page. A watchlist link will be provided on the resulting page. (Note: you must be logged in to view your watchlist.) Click it, and you will be taken to a page that shows changes to all pages you have selected to watch. On the watchlist display, some page names appear in bold. This indicates that you have not visited the page since changes were made to the entry. View history One final way to monitor changes made to that Alaska 2D land line entry (or any other entry in the SEG Wiki) is to use the View history tool. Once you have landed on a wiki entry of interest, you may view the entire history of the entry, from its initial inclusion in the wiki throughout its entire contributor-driven development up to its current state by clicking the View history link at the top of the Web page. On the resulting page, you will receive the same supplementary information provided on the Recent changes page, but with a specific focus on the entry you are currently viewing. Part of the attraction of a wiki is being able to monitor its evolution over time as your colleagues perpetually contribute their expertise. Track changes, Watch, and View history are three tools that can help you do so.
Corresponding author: [email protected]

Track changes Say you’ve been keeping your eye on an interesting SEG Wiki article about the Alaska 2D land line. You visited the page once, and then revisited the entry two weeks later. At that time, you noticed that changes had been made to the article. Of course, on your initial visit, you didn’t take the time to memorize the content word-for-word, so now you’re not entirely sure which content is the original from Sheriff’s Encyclopedic Dictionary and which is from your esteemed colleagues. By clicking the Recent changes link in the navigation on the left side of the page of the wiki, you can see exactly which changes have been made to the article in question (and to other articles), and you can see exactly when the changes were made. To make sense of the information provided on the Recent changes page, there are a few abbreviations and notations with which you will want to be familiar: • “diff” displays the difference from the previous revision of the page. • “hist” links to the revision history of the page. • The full title of the page links to the current version. If you are “watching” the page, the title appears in bold. Some changes will also include the following notations: • “N” denotes a new page. • “m” denotes a minor edit. • “b” denotes an edit made by a bot.
1120 The Leading Edge September 2012

Understanding Reservoirs
Reservoir solutions for unconventional resources
Heavy oil, shale and coal seam gas reservoirs require precise subsurface information to optimize drilling and maximize production. With the most experience in survey design, acquisition, advanced imaging and reservoir characterization for unconventional resources, CGGVeritas has a proven track record for delivering the best possible solution for complex imaging. With local expertise in the regional geology and a portfolio of innovative technologies, including continuous reservoir monitoring and microseismic services, we employ a tailor-made solution to help you understand the unique qualities of the unconventional asset you are developing. Understand more by solving together.

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Delivering SeisAble Benefits