Michael R. Walls

Corporate risk tolerance and capital allocation: A practical approach to implementing an exploration risk policy

Petroleum exploration companies are confronted regularly with the issue of allocating scarce capital among a set of available exploration projects, which are generally characterized by a high degree of financial risk and uncertainty. Commonly used methods for evaluating alternative investments consider the amount and timing of the monetary flows associated with a project and ignore the firm`s ability or willingness to assume the business risk of the project. The preference-theory approach combines the traditional means of project valuation, net present value (NPV) analysis, with a decision-science-based approach to risk management. This integrated model provides a means for exploration firms to measure and to manage the financial risks associated with petroleum exploration, consistent with the firm`s desired risk policy.

Managing Technological and Financial Uncertainty: A Decision Science Approach for Strategic Drilling Decisions

This paper presents a framework to improve the quality of investment decisions in petroleum. The model presented enables the decision-maker to explicitly consider two major objectives when evaluating new petroleum opportunities—financial and technological gain. We utilize MultiAttribute Utility Theory (MAUT) to consider simultaneously the technological challenges of petroleum exploration into the capital budgeting process of an exploration and production firm. The MAUT methodology presented in this work demonstrates that in some mature areas the advantages to exploration are restricted further only to financial gain, based upon the present economic potential of the basin. On the other hand, other seemingly less attractive areas, such as deep horizons in deep-water basins, may represent attractive targets for new exploration as a result of the interaction of financial gain and technological advancement. This advantage reflects the technological gain as a key factor for future operations for oil discoveries in areas with big geological potential. The model presented in this work enables the decision-maker to consider explicitly the risk and rewards associated with both financial and technological payoffs, the decision-makers tolerance for those types of risks, and the relative importance of each of those objectives in the context of ongoing petroleum exploration decisions.

Integrating Business Strategy and Capital Allocation: an Application of Multi-Objective Decision Making

ABSTRACT A fundamental element of the business strategy process is allocating capital and corporate resources. Managers of this process are often faced with complicating factors such as financial risk and uncertainty about potential outcomes. This paper describes a decision science methodology for systematically integrating the processes of business strategy and capital allocation. We present an application of this methodology by a large oil and gas company concerned with allocating an annual

IMPROVING SYSTEM MAINTENANCE DECISIONS: A VALUE OF INFORMATION FRAMEWORK

200 million capital budget. This approach is shown to have broad implications for managers in all sectors and provides rich insight into the effects of integrating corporate objectives and risk policy into the investment choice process.

Risky choice, risk sharing and decision analysis: Implications for managers in the resource sector

ABSTRACT Decisions concerning maintenance strategies have become increasingly important as managers face more complex systems in the manufacturing, construction and process industries. The choice among maintenance methods, reactive, preventive or predictive, is often complicated by uncertainty about component failure, reliability of the predictive sensor readings, and the potential for significant financial exposure. This paper describes a decision-science methodology for evaluating alternative maintenance strategies. We use a value of information framework to analyze the maintenance decision in the context of the choice alternatives maintenance managers face, the key sources of uncertainty associated with component failure and sensor readings, and the associated maintenance and repair costs. This work presents an application of the decision model to a gantry crane system used at a hazardous waste retrieval site. This methodology can guide managers toward a more formal evaluation of maintenance methods, g...

Developing an exploration decision support system (EDSS): A strategy for combining information and analytics

Abstract This paper provides a brief overview of decision and preference analysis concepts and demonstrates an application of these techniques to the project-valuation problem faced by resource managers. Our major focus is on the use of the exponential utility function, the utility function most frequently used by resource companies. We discuss the important and practical risk-sharing problem faced by managers in the resource sector, that is, how to choose the optimal share of a risky project. We demonstrate that with decision and preference analysis tools it can be quite straightforward for managers to identify their optimal share in risky projects. We then explore these techniques further and demonstrate that they can lead to some seemingly counter-intuitive results. In particular, we explore how the firms optimal share changes with exogeneous changes in project parameters. What we find is that while many of the changes in share are intuitive, some are not. In fact, when the firms estimate of the potential upside payoff upon finding reserves increases, it is sometimes better to decrease the firms share than it is to increase it. This is important, because by recognizing this counter-intuitive result, we can work to improve our intuition by understanding it. We summarize our findings and offers some guidelines resource managers should consider when considering a choice of utility function.

Risk Propensity and Firm Performance: A Study of the Petroleum Exploration Industry

Petroleum exploration companies enter the twenty first century facing an increasingly competitive and risky environment. Under those circumstances, there is a growing need for better systematic decision-making that explicitly embodies the firms desired goals and resource constraints. Computer-aided decision making, or decision support systems (DSS), provide an aid for those exploration management problems that are large, complex, unstructured, and involve management mudgment. Almost every present day DSS falls into one of two general classes. Vehicle DSSs such as linear/nonlinear programming models and other optimization routines, propose and impose specific methodologies to the decision-maker. On the other hand, toolbox DSSs, such as simulation programs, statistical functions, and graphical packages, are generally flexible in enabling their users to employ a variety of approaches and tools for their decision tasks but provide little guidance on both problem representation and investigation. This paper describes the development of a hybrid DSS model that combines the advantages of both the vehicle and toolbox systems components to provide a comprehensive approach to exploration planning from geological development through the capital allocation process. The Exploration Decision Support System (EDSS) preserves the flexibility of the toolbox system while enriching the problem-solving strategies available to the firm. The central objectives for developing an EDSS framework are: (1) better decisions about resource allocations; (2) more systematic understanding of the factors affecting exploration decisions; (3) improved communication about E&P performance objectives and constraints at all levels of decision-making; and (4) an explicit vehicle for continuous improvement of the petroleum exploration firms decision-making process. The EDSS model can guide geological and exploration managers toward a more formal evaluation of projects, provide insight into the impact of competing choice alternatives, and significantly improve the quality of exploration decisions.