This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 110765, "Modeling the Economic Impact of Cognitive Biases on Oil and Gas Decisions," by M.B. Welsh, SPE, and S.H. Begg, SPE, University of Adelaide, and R.B. Bratvold, SPE, University of Stavanger, prepared for the 2007 SPE Annual Technical Conference and Exhibition, Anaheim, California, 11-14 November. The paper has not been peer reviewed.
Cognitive biases are known to affect decisions made under conditions of uncertainty. Previous demonstrations of these biases have focused on their effect on a single-parameter, typically technical, judgment rather than examining the potential effect on economics when applied to all of the judgments involved in a complex oil and gas (O&G) decision such as reservoir characterization. Three individual biases—overconfidence, trust heuristic, and availability heuristic—were modeled to measure their effect on economic outcome as represented by net present value (NPV).
Cognitive biases are unconscious errors in judgments and decisions (particularly those made under conditions of uncertainty) that arise from the inherent structure and functioning of the brain's cognitive architecture. These errors affect most subjective judgments of probability and value and can be multiplicative—the same biases affecting multiple parameters, thus increasing the effect with the complexity of calculations made with those parameters.
The effects that such biases can have on the complex decisions in the O&G industry, however, have not been assessed. Part of the reason is the perceived difference in scale between simple judgments, on which biases operate, and large exploration and development decisions. This difference is, however, largely illusory because the largest industry decisions, at their base, depend on the judgments of individuals and are, thus, vulnerable to any biases that affect those individuals. Many industry decisions are made by teams, but studies suggest that, in many cases, the "team" decision actually is based largely on the judgment of the most trusted member of that team.
Decisions that seem particularly vulnerable to biases are choices of distributions for input parameters that go into simple volumetrics calculations. Such parameters include average porosity, net-/gross-thickness ratio, and formation volume factor. Given the high degree of subjectivity in assessing these distributions and the difficulty of calibrating the assessments, they are extremely susceptible to cognitive bias. The problem for large O&G decisions is that they rely on many such subjective estimates, all of which are likely to be affected by bias.
Modeling Biases. Three biases were chosen for modeling: overconfidence, trust heuristic, and availability heuristic. The models used for overconfidence and availability heuristic differ slightly but were run using an offshore-development decision regarding a large, but economically marginal, field.
To limit the scope of the problem, bias and uncertainty were restricted to parameters affecting volumetrics. The development characteristics were determined by the interplay of reserves according to realistic rules (including determining the number of wells appropriate to a discovery of a certain size) and by capacity limits, development schedule, and pressure depletion.