Debiasing probabilistic oil production forecasts

Abstract

Exploration and production companies in the hydrocarbon industry have every interest in producing unbiased production forecasts at the time of the investment decision, since it is an intrinsic part of making oil field development profitable. However, recent results show presence of significant biases in the uncertainty models which support these decisions. Some important questions which are addressed in this study are i) whether there are simpler and more robust approaches to forecasting than what is the practice in this industry today, ii) whether forecasts can be calibrated for bias, and iii) what the consequences are for valuation of investments in new oil fields. In this study, 71 oil fields on the Norwegian continental shelf with production start between 1995 and 2020 are analyzed. Three robust bias reduction methods are proposed: a pure reference class forecast and two calibration models for the field operators' own forecasts. These show that expected production early in the field lifetime must be shifted down and that the uncertainty range must be expanded. The results are also consistent across field sizes and over time. The findings in this study demonstrate the need to draw on results in behavioral economics to improve uncertainty quantification - reference class forecasting is an inexpensive and powerful way to avoid cognitive biases. An important conclusion is also that the discounted revenue stream from new oil fields is far more uncertain and has a lower expected value than companies lay to ground.