Significance of Project Risking Methods on Portfolio Optimization Models
- P.A. Tyler (Schlumberger GeoQuest) | J.R. McVean (Merak Projects)
- Document ID
- Society of Petroleum Engineers
- SPE Latin American and Caribbean Petroleum Engineering Conference, 25-28 March, Buenos Aires, Argentina
- Publication Date
- Document Type
- Conference Paper
- 2001. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 5.7.5 Economic Evaluations, 7.10 Capital Budgeting and Project Selection, 7.1.5 Portfolio Analysis, Management and Optimization
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Efficient frontier theory is gaining acceptance as a part of the portfolio analysis process in the petroleum industry. One of the major difficulties companies encounter in creating corporate efficient frontiers is in representing project level risks in a corporate consolidation of value. Full stochastic evaluations require the management of huge quantities of data. As a result several current solutions use multiple discrete outcomes to represent any given project, rather then a complete stochastic distribution. This paper evaluates the differences resulting from using these two approaches to project risking. By examining the optimized portfolio results and the resultant efficient frontier, we are able to draw direct conclusions about the added value attributable to detailed Monte Carlo based evaluations. Since price is the source of most correlation between projects, the investigation was done with and without price as an input variable. It was discovered that the differences between the stochastic and discrete outcome evaluations are not significant when price uncertainty is neglected, but the inclusion of price leads to significant differences if the resulting correlation is not accounted for.
Petroleum managers are constantly faced with the decision of how to invest limited amounts of capital in order to maximize shareholder value or return. This is usually done by evaluating all the available investments, and then selecting a subset in which to invest. This subset is often referred to as the company's portfolio. Selection of this portfolio is critical to a company's success and therefore it requires significant consideration.
The goal of the portfolio selection process is to select the "optimal" set of projects. However, this is not a simple process, and selecting the "optimal" portfolio while staying within the corporate strategy and constraints can become a very daunting exercise. The task of matching project selection to the company strategy and goals is often referred to as Portfolio Management. There are many elements that must be taken into account in portfolio management, including.
Maximizing the value of the portfolio.
Living within the capital spending limits.
Meeting the production requirements.
Achieving both short term and long term cash flow goals.
Matching forecasted net income targets.
Meeting developmental and/or environmental constraints.
This is even more complex in our industry where many projects have significant amounts of uncertainty, or variation in the possible outcomes. In recent years more and more attention has been placed on how to represent these uncertainties within the portfolio evaluation. These uncertainties exist in various forms within the investment projects of a petroleum company, and can include.
Existence of hydrocarbons (probability of success).
Geological reservoir properties.
Timing and extent of the development program.
Capital and operating costs.
Oil and gas prices.
These uncertainties in the input data required to make an economic evaluation of E&P projects lead to uncertainties in the economic results. Acknowledging that these uncertainties exist in the individual projects, naturally leads to the concept that uncertainties exist in the overall portfolio. In recent years a significant amount of effort has been spent in trying to define this portfolio risk and to compare portfolio's by their risk vs. reward relationship.
In light of the importance of portfolio level risk, this paper compares two differing approaches to project evaluation under uncertain conditions, and the effect the differing approaches have on the overall portfolio risk measurements. The two approaches compared will be an uncertainty tree approach where projects are evaluated as distinct outcomes each with a differing probability of occurrence (referred to here as "simple stochastic"), and a full Monte Carlo evaluation approach where the projects are evaluated across the full range of input uncertainties.
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