Abandonment Decisions and the Value of Flexibility

Abstract

Abstract This paper describes an investigation of abandonment decisions and shut-in policy as a function of uncertainty in oil price. We first review a fundamental error that is often made in predicting the outcome of, and hence making decisions about, systems that are subject to uncertainty: for many common models, the use of "best" estimates of the uncertain input parameters to the model does NOT result in the "best" estimate of the model's output ("best" is defined as average, or minimum error). The same argument applies to predicting output statistics, such as P10 or P90, from corresponding input statistics. This is part of the reasoning behind, for example, the use of geostatistical simulation models of the sub-surface, rather than smoothed, spatially-averaged models. In this work the focus is on decision errors caused by temporal averaging, specifically, the "smoothing out" of oil price fluctuations over time, and by restricting uncertainty investigations to the uncertainty in parameters of smoothed price models. We illustrate these points by application to determining optimal abandonment decision policies. We show that it is better to wait for a period after first going cash-flow negative, and how to estimate the length of that time. We also show that these conclusions are relatively insensitive to the oil-price model parameters. Further we show that, if maximizing NPV is the objective, then contrary to normal operating procedures, it is more economic to choke-back production in periods of low oil price. Introduction This paper centers around two fundamental issues regarding how uncertainty is dealt with when using model-based predictions of economic value in making decisions. The first is around the benefits of designing flexibility into projects to generate the option of making return-maximizing decisions, as uncertainties are resolved over a project's lifetime. The second is a related requirement that, due to the non-linear nature of most models involved in decision-making, it is important to account for uncertainty not by expected (average) values of uncertain quantities, but by modeling the full range of possible outcomes. We illustrate these two issues by investigating the impacts of uncertainty on optimal abandonment decision policies. Uncertainty and Industry Performance Through anecdotal stories, internal company reviews and first hand experience, many people in our industry are familiar with projects that failed to return the predicted technical and economic metrics that formed the basis of the investment decision. However, published data are rare. Some harder evidence of industry performance comes from a study by Merrow1, who reviewed over 1000 E&P projects, whose CapEx ranged from $1Million-$3Billion. He shows that many failed to deliver the performance they promised, and that one-in-eight projects were "disasters", where "disaster" is defined as the project failing on 2 out of the following three metrics:>40% cost growth>40% time slippage1st year "operability" < 50% of plan The average CapEx for these projects was $670Million. Even worse, over half of the biggest projects (CapEx > $1Billion) were "disasters". A more pernicious form of under-performance occurs when projects do meet investment criteria, but fail to achieve the performance levels that could have been possible. This may be due to a culture of "satisficing"2, where decisions are made that are good enough to justify the investment, but are significantly sub-optimal. Another factor that might cause this type of under-performance is an over focus on mitigating the risks that arise from uncertainty compared to efforts to capture its upside. We have previously argued3,4 that the root cause of the failure of many projects to achieve their optimal performance is uncertainty, in its broadest sense, which leads to over-estimating returns or under-estimating the risks of loss. The key to improving returns is better decision-making under uncertainty (uncertainty around current "states-of-nature", future predictions and uncertainty around the likelihood of implementing projects as planned).