Quantification of oil price uncertainty in economic evaluation using sequential Gaussian simulation
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In this paper, we propose improved methodology for quantifying the uncertainty in petroleum economic evaluations due to uncertainty in future oil prices. Conventional "hockey stick price forecasts commonly used in industry significantly underestimate uncertainty because they do not reproduce the volatility inherent in oil prices. Some authors have proposed stochastic methods, such as the bootstrap method, to better model oil price volatility. A disadvantage of the bootstrap method, however, is that it can produce price realizations with unrealistically low or high prices. To address this shortcoming, we present two stochastic methods that honor both the historical distribution of oil prices as well as the historically observed variability in oil prices. The first is an approximate method in which multiple future price realizations are taken directly from different windows of historical uninflated price data. The method is easy to apply and can provide greater insights into the uncertainty and risks associated with project economic evaluation. However, this method can potentially overestimate uncertainty in oil prices and should be used judiciously. The second method uses sequential Gaussian simulation to generate equiprobable future oil price realizations consistent with both the frequency distribution and temporal variability of historical prices. Although the method is more difficult to apply, it provides a more statistically sound basis for quantifying uncertainty. The improved methods are compared to conventional methods for three synthetic cases derived from the literature and a field case. Results demonstrate that conventional methods underestimate oil price uncertainty more severely for projects with accelerated cash flow streams, which characterize most petroleum development projects. This indicates that probabilistic methods for quantifying uncertainty in oil prices, such as the sequential Gaussian simulation method proposed here, are needed to provide operators with reliable quantifications of the uncertainties and risks associated with individual projects, which should enable improved investment decision making.
