Applying Integrated Project-Management Methodology to Hydrocarbon-Portfolio Analysis and Optimization
Sholarin Ebenezer Adekunle, Curtin U. of Technology
Aligning hydrocarbon investments with constantly shifting business goals and priorities continues to be a major challenge for E&P staff. In spite of the best efforts of international oil companies (IOCs) to rebuild in-house engineering and project-management capability, an unacceptable number of oilfield-development initiatives continue to either fall short of or simply miss their targets. This is exacerbated by insufficient supply of technical staff with experience in managing large and complex projects. There is no end to the range of factors that can contribute to project failure, and as a result, the industry has invested heavily to improve the predictability, producibility, and quality of their projects. While techniques such as estimating, risk assessment, process management, deliverables management, and project management improve project execution, they fail to address the more critical issue of scheduling and achieving satisfactory cost performances.
Petroleum exploration and field development deal with many unknowns. With so many risks and uncertainties associated with income and life-cycle cost factors such as potential reserves, capital expenditures (capex), operating expenditures (opex), production rate, oil and gas pricing, geological success ratios, and intervention-vessel expenses (especially for subsea wells), it is extraordinarily difficult to forecast earnings and cash flow for even the simplest of prospects. The management of risk in petroleum ventures always has been a difficult subject, and it is even more important in these days of massive cost overruns and overwhelming delays. Although petroleum upstream operations have not been a traditional area for the conventional practice of project-management techniques, recent literature has shown an increasing rate of embracing project-management principles in exploration, drilling, and production operations. The techniques that traditionally have been limited
to construction-type projects are now being applied to petroleum operations with increasing frequency. In the past, E&P staff were charged with the responsibility of exploring and producing hydrocarbons. However, the present challenge has gone beyond finding and developing barrels or molecules: They now must make complex decisions about managing technical performance, risk, economics, and corporate strategy. Decisions are best made when projects are appropriately risk-evaluated, consistently described in economic and technical terms, and assessed relative to how they interact through time with other investments to deliver value to the company. Merging risk assessment and operational management is a critical first step. The project objectives, or the measure of project success or failure, are often defined in terms of cost, schedule, and technical performance. Risk management, on the other hand, is intended to increase the likelihood of attaining these objectives by providing a systematic approach for analyzing, controlling, and documenting identified threats during both the planning and execution of a project. The project-management process itself will remain constant in different phases throughout the project life cycle. Various project-management tools and techniques used for planning, evaluating, and controlling also will remain constant. What will vary, however, over the project life cycle is the quality of the available risk-related information, the kind of competence that is needed to compile and filter information, and the kind of decisions that are supported by the project- and risk-management activities. Modern Portfolio Theory In 1952, an economist and Nobel laureate, Harry M. Markowitz, revolutionized modern investment theory and
Fig. 1—Diagram of efficient frontier.
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practice by suggesting that each portfolio of assets would have a given level of risk and reward, but that for any level of risk there was only one portfolio that would return an optimum reward. Conversely, for any level of reward, there would be only one portfolio that would minimize the risk. Among 20th-century financial analytical tools, modern portfolio theory is regarded as the most important. Portfolio optimization originally was created as a securities investment optimization tool. It is based on the theory that given the expected return and risk of a security, along with its correlation with other securities in a portfolio, a selection of stocks can be chosen that maximizes the return for any level of risk. The fundamental objective of the portfolio-optimization application used for this study was to derive the optimal mix of investment opportunities that minimized risk for a given level of return, or maximized return for a given level of risk. Oil and gas companies traditionally allocate capital to projects if present hurdle rates are achieved by certain economic indicators, such as net present value (NPV), internal rate of return (IRR), or return on investment (ROI). When capital is allocated in a portfolio-budgeting process, there are usually more opportunities available than capital to fund them. In such circumstances, E&P decision makers long have been advised to rank their project investment opportunities by a metric reflecting the marginal return to capital employed (NPV divided by investment is popular) and to fund the projects in descending order until the available capital pool is exhausted. This approach maximizes the economic return and provides the firm with the maximum expected value creation for given investment. With regard to risk, however, the message to E&P decision makers was very clear: Risk should be managed in a proactive manner. The point is not to avoid risk, but to take the high risks, which will have a substantial impact on the firm’s cost of capital, and manage them professionally. Over the past 2 decades, a different view has emerged, one that takes explicit account of risk. Monte Carlo simulation, decision-tree
analysis, and modern portfolio theory have been proved to enhance petroleum investment decision making. Portfolio-optimization theory will provide an insight into the following questions: • What is the best mix of exploration opportunities and development opportunities? • How will a new exploration program affect overall investment strategy? • What is the optimum mixture of domestic and international opportunities? Since projects are a combination of “unique” discrete entities, an optimal portfolio is a point in the feasible set of projects. Therefore, the feasible set of hydrocarbon portfolio is a set of points that form the efficient frontier rather than a continuous line as developed by the theory. Fig. 1 displays a typical efficientfrontier chart. The horizontal axis represents risk (typically in the form of the standard deviation of return in dollars), while the vertical axis represents return (typically in the form of the mean return expected of the portfolio). The line, which depicts the efficient frontier itself, represents the highest level of expected return for any given level of risk for any possible combination of assets in the portfolio. The efficient frontier is the curve of optimal risk/ return tradeoffs, from which a decision maker can select the one that best suits his/her tolerance for risk. Using Monte Carlo Simulation A Monte Carlo simulation technique is used to estimate the oil and gas volumes that would be delivered from the projects under review on a yearly basis and the life-cycle costs associated with producing these volumes. All failures and incidents that would result in loss of production relative to specified market demand were included. In this study, a number of sensitivities were run. Individual parameters were varied one at a time, keeping all other parameters fixed at their base values, while sensitivity analysis was taken from a Monte Carlo simulation during which all stochastic inputs vary simultaneously. The sensitivity analysis gave a fuller
account of the total picture because correlations and interactions between inputs are also included (Fig. 2). Results were most dramatically impacted by oil revenue, followed closely by opex. The main significance of a sensitivity chart is that it provides visual representations of the impact the variables are expected to have on a given economic indicator. Scenarios for reserves, oil and
gas prices, capex, opex, and discount factor are important parameters that need to be included in a sensitivity chart. With Monte Carlo simulation, all parameters were varied simultaneously with individual values generated randomly from probability distributions over many trials. Fig. 3 shows a cumulative probability plot of the NPV generated from the aggregation of the
TABLE 1—OPPORTUNITY SET OF INVESTMENTS Project Name 1 2 3 4 5 Total Capex ($ million) 100 200 75 150 50 $300 million NPV @ 10% ($ million) 42 40 35 20 30 Ranking by NPV PWR @ 10% 1st 2nd 3rd 5th 4th $82 million 0.42 0.20 0.47 0.13 0.60 Ranking by PWR 3rd 4th 2nd 5th 1st $122 million
individual input variables. From this plot, the P10, P50, and P90 NPVs can be calculated graphically. Portfolio-Optimization Model Five investment opportunities, a mix of natural-gas/oil E&P projects, were evaluated under various scenarios to develop a robust understanding of an upstream-project portfolio. Their NPV and present-worth-ratio (PWR) (NPV divided by capex) indicators are displayed in Table 1. The table shows that each of the projects responded as expected under the respective scenarios. A fundamental objective of the portfolio-optimization application used for this engagement was to derive the optimal mix of investment opportunities that minimized risk for a given level of
return, or maximized return for a given level of risk. Using OptQuest computer optimization software, a portfolio optimization was conducted for the same group of projects, resulting in an “efficient frontier,” the locus of portfolio selections for which no lower risk selection is available with as great a return and no selection with a higher return is available without higher risk. In this case, risk was estimated by standard deviation of portfolio NPV (consistent with Markowitz’s theory) as calculated across various scenarios. At the portfolio level, a budget of $300 million was imposed, enabling one-to-one comparison of the computer-optimized portfolio solution to the PWR portfolio. If evaluated on a standalone basis, Project 5 has the highest
value per dollar invested. Under traditional decision making, for a riskaverse investor, Project 5 would be preferred over other projects. Although Project 1 has the highest NPV , its PWR is less than Projects 3 and 5. However, when viewed from the portfolio framework, the results are different. Fig. 4, the risk/return plot, shows that the conventional decision rule does maximize return as expected, but, in ignoring risk, it also maximizes risk. The points from this efficient frontier have also been overlaid onto Fig. 5 to allow easy comparison. Portfolio “a” represents an optimized opportunity set generated by selecting projects according to their PWR ranking. This opportunity mix includes three projects and has little or no risk compared to other portfolios with similar returns. Portfolio “b” is an opportunity mix of Projects 1 and 2 and is the selection of projects when ranking them by NPV. This solution has 31% more risk than the portfolio based on PWR ranking. The other portfolios (“c,” “d,” and “e”) all have high values for their NPV but also have high levels of risk in comparison to Portfolio “a.” Besides illuminating the overall risk and return tradeoffs, portfolio analysis provided another significant insight: Project selection could be compared within and across efficient portfolios, and the relative project importance could be determined. In addition, the use of sensitivity analysis in portfolio optimization enabled the impact of existing and new projects to be explored and various objectives and constraints to be re-examined. This triggered new and creative efficient frontiers and informed management of more-robust portfolio strategies. Conclusion This article describes how to integrate project-management principles and risk-assessment techniques into decision making for selecting and optimizing oil and gas investment projects. The findings of this study were: • An approach using sound projectmanagement method, life-cycle economics, and stochastic optimization in a probabilistic fashion can provide
Fig. 4—The risk/return plot.
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Fig. 5—Overlay chart of portfolio NPVs.
a useful tool for planning and tracking complex oil and gas projects. • All project plans must form an integral part of every company’s strategic plan. Oil and gas companies must be committed to project-management principles by investing in projectmanagement training, certification, performance improvement, and tools for planning, tracking, and controlling complex projects. • Just as with 3D seismic, portfolio modeling with the aid of “efficient frontier” technique can help optimize the critical resources and provide decision makers with sensible options that ensure that projects are executed as efficiently and effectively as possible—on target, on time, and on budget. JPT