TX 75083-3836, U.S.A., fax 01-972-952-9435. SummaryThis paper addresses the need for a holistic, integrated approach to assessing the impacts of uncertainty on oil and gas investment decision-making. We argue that this cannot be accomplished effectively by just adding a capability to deal with uncertainty to classical, rigorous models of all the components that contribute to an investment decision evaluation. Further, we suggest that such an approach, if feasible, is not desirable. Instead, we propose the concept of a Stochastic Integrated Asset Model (SIAM) embedded in a decision support system. This approach involves trading-off some technical rigor for a more complete and accurate assessment of the impacts of uncertainty on the investment decision-making process. The main elements of the system are: simplified component models for each domain; Monte Carlo simulation engine; modeling language for customization, incorporation of interdependencies between components, implementation of decision logic and updating information as a result of learning. We illustrate how such a system identifies which uncertainties impact the decision the most; values the acquisition of information (data, technical analysis) and encourages flexibility in go-forward plans to mitigate and/or exploit uncertainties. Further applications are to the optimization of development plans, real options valuation and the generation of consistent, risked cash flows for input to portfolio analysis. We believe that application of such a system results in a true value-driven focus to the work of multi-disciplinary asset teams through its ability to integrate the technical and business aspects of decisions.

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper is part of an overall programme to investigate how to distill the massive amounts of technical information that are generated in the analysis of any upstream petroleum asset into suitable formats for use in "complete" decision tree analyses of the design, management and value of that asset. A complete decision tree analyses future flexibility in response to all dynamic uncertain variables, including prices, throughout the life cycle of the asset. The first part of the paper explains how and why we have set up this programme. The second part of the paper begins an examination of a particular issue where we need to distill technical detail for use in a complete decision tree.The upstream petroleum asset life cycle is usually divided into discrete phases. However, the actual situation is more fluid. For example, information arrives, and can be collected, throughout the life of the asset. In this sense, appraisal never stops, and, in principle, development and production activities should be tuned to take into account the value of the information that can be collected as a result of these activities.We begin to explore this by examining, in an integrated fashion, the appraisal and development phases of the asset life cycle, using a model of an offshore oil-field development lease as an example.We presume that drilling and facilities construction are the only two activities during this part of the life cycle.Drilling can give information about the asset ("appraisal drilling") or provide production capability ("production drilling") or both.Investment in production facilities begins at sanction, which can occur in any year until the end of the lease.In our initial exploration of this issue, we have found situations where an asset manager can add value by considering the option to have mixed appraisal and production drilling programmes before and after sanction.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis 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.