Modeling the Economic Impact of Cognitive Biases on Oil and Gas Decisions

SPE Paper number 110765-MSTaken under conditions of uncertainty [1, 2]. Previous demonstrations of these biases, however, have focused on their impact on a single, typically technical, parameter judgment rather than examining their potential impact on economics when applied to all of the judgments involved in a complex Oil and Gas (O&G) decision such as reservoir characterization. Herein, we model the impact of three individual biases – overconfidence [2], bias resulting from the trust heuristic [3] and availability [4] - to measure their effect on economic outcome as represented by NPV. All three biases are shown to have marked effects on the estimated value of a project, not just its uncertainty. Overconfidence at the levels people commonly demonstrate shown to result in a $259 million dollar mistake in calculating NPV in our modeled decision – making a project with a negative NPV seem to have a positive NPV. Availability bias, as modeled herein, similarly acts to increase the apparent NPV of a project – making a project actually likely to result in a $55 million dollar loss seem to have an NPV of up to $329 million. The trust heuristic differs slightly in that its use, rather than actively causing bias, prevents the use of techniques that reduce overconfidence. The model shows that incorporating the views of five experts rather than accepting the values provided by the best of the five reduces overconfidence by 10%, even when agreement between the experts is high. Referring this back to our overconfidence model results, we note that this equates to a difference of up to $125 million in the mean NPV of the project. On the basis of our findings, we argue that industry personnel be educated in the psychological limitations affecting decision making as awareness of their existence is the first step in reducing their effect [5]. Further, the inclusion of such material in petroleum engineering and geosciences curricula would be an effective way of ensuring that knowledge of cognitive biases spreads across the industry [6].M.B. Welsh, and S.H. Begg, and R.B.Bratvol