Sensitivity of Project Economics to Uncertainty in Type and Parameters of Oil Price Models

Abstract

Abstract Dynamic price models (which replicate the characteristics of real price fluctuations over time, not just the mean) are a crucial element in economic evaluations. However, there has been little systematic evaluation of the effects of uncertainty in what type of model is most appropriate, or of uncertainty in the parameters of such models. We present the results of a sensitivity analysis of economic metrics to uncertainty in both type of oil price model and in the values of the model parameters. Uncertainty in both arises from arbitrary choices in their derivation from historical data and from uncertainty as to whether the past predicts the future. We take four types of price model (simple probabilistic, Geometric Brownian Motion, mean reverting, mean-reverting with jumps) and use historical data to derive estimates (and uncertainty therein) of the numerical values of their respective parameters. The impact of model choice and parameter uncertainty is then compared via their impact on economic metrics for a typical field development decision.