Bart J.A. Willigers

Creating Efficient Portfolios that Match Competing Corporate Strategies

The process of portfolio optimization provides guidance to decision makers on how to manage an asset base given corporate objectives, market conditions, and organizational capability. Many applications in the oil and gas industry are based upon Markowitz’s efficient portfolio theory. In the standard implementation of this framework, an efficient portfolio is defined as one that yields the highest value given a specific degree of risk. A corporate decision maker will aim, however, to select a portfolio that meets several often competing objectives, i.e., maximize portfolio value while minimizing capital expenditure. The optimal portfolio choice given one constraint is typically not optimal given one of the competing constraints. This requires the portfolio manager to identify and select those portfolios that best meet all corporate constraints. Deciding which portfolio to develop is often compounded by there being several portfolios having similar economic characteristics. However, these portfolios can generally be differentiated by strategy, which may depend on nonfinancial attributes such as the geographic location of the assets or on geological settings that might require different engineering expertise. In this study, a large set of exploration portfolios and their attributes have been simulated. Through applying a series of simple and transparent filters, a few portfolios can be identified that meet all the corporate constraints. After a shortlist has been created, the portfolios can easily be characterized by strategy and the tradeoffs between them can be assessed.

A Game Theoretic Approach to Conflicting and Evolving Stakeholder Preferences in the E&P Industry

Summary In the high-risk E&P industry, the profit of each stakeholder depends on the strategies of all. The optimal choice for one player may not be optimal for other players, who may opt to prevent it. These characteristics suggest using game theory to model decision situations in the E&P industry. In a business dominated by joint ventures and tight governmental regulation, an understanding of the interests and influence of all stakeholders is particularly important. Although the E&P industry is accustomed to developing complex economic models to obtain insight into the commercial attractiveness of joint ventures, the influence of other stakeholders is often ignored. Each stakeholder must decide how much to invest, in which sequence to make decisions, whether to make decisions before or after the other stakeholders, which decisions to make before vs. after technological and organizational uncertainty is resolved, whether to accept or block the designation of one stakeholder as dominant, and whether to accept or block hub placement within one of multiple oil fields. A joint development program of a portfolio of gas fields and its gas processing and export facility has been analyzed using tools from game theory. The focus is on which infrastructure to develop and when. Players’ preferences are functions of equity stakes and expected reserve sizes of the prospects. The analysis provides insight into the preferred development options of the individual players, how preferences change as uncertainty gets resolved, and how much individual players are to gain or lose if certain investment decisions are made. The analysis allows a player (1) to identify under what conditions its objectives are aligned with fellow players, and (2) to quantify the maximum amount it can pay to gain support from its fellow players.

Valuation of swing contracts by Least Square Monte Carlo simulation

This paper was accepted for presentation at the 2010 Asia Pacific Oil & Gas Conference and Exhibition held in Brisbane, Queensland, Australia, 18–20 October 2010, and revised for publication. Original manuscript received for review 2 December 2010. Revised paper received for review 2 August 2011. Paper peer approved 15 September 2011 as SPE paper 133044. Summary Natural gas and electricity are commonly traded through swing contracts that enable the buyer to exploit changes in market price or market demand by varying the quantity they receive from the producer (seller). The producer is assured of selling a minimum quantity at a fixed price, but must be able to meet the variable demand from the buyer. The flexibility of such contracts enables both parties to mitigate the risks and exploit the opportunities that arise from uncertainty in production, demand, price, and so on. But how valuable are they? Traditional net present value (NPV), based on expected values, cannot value this flexibility, and the traditional options/valuation techniques could not model the complexity of the terms of such contracts. Taking gas contracts as an example, this paper seeks to (a) raise awareness of how flexibility creates value for both parties and (b) show how least-squares Monte Carlo (LSM) simulation can be used to quantify its value in dollar terms, from the perspective of both producer and buyer. Because the value of flexibility arises from the ability it gives to respond to fluctuations (e.g., in commodity prices), a useful model of swing contracts needs to reflect the nature of these fluctuations.