Calibration Improves Uncertainty Estimation Through the Reservoir Life Cycle
Authors: McVay Duane A. 
Published: October 2008

In this paper we describe a calibration process to improve estimation of uncertainties associated with reservoir and economic performance predictions. The need for such a calibration process arises from our observation that, despite recent advances in uncertainty quantification, we continue to underestimate the uncertainties associated with petroleum asset management. Calibration of uncertainty estimates can be achieved through a continuous process of data acquisition, model calibration, model prediction, economic forecasting, and uncertainty assessment, which is similar to the process employed in weather forecasting. Improved ability to estimate uncertainty should result in better investment decision making and, ultimately, increased profitability.

Introduction

With oil and gas exploration and development moving to harsh, frontier areas such as deep water and Alaska, energy companies are investing more and more capital for hydrocarbon extraction. Investment decisions throughout the life of a reservoir are made in the face of significant uncertainty and risk. While acquiring data to reduce uncertainty is an obvious goal, it is equally, if not more, important to accurately quantify the uncertainty associated with the decisions. This allows companies to make the best choices from a portfolio of uncertain investment opportunities.

Quantification of uncertainty means defining, in some way, the potential variance in an estimated quantity, e.g., reserves. The potential variance is often expressed as a range of values corresponding to a particular probability level, e.g., 90%. In other words, there is a 90% probability that the "right" answer is within the range of values specified.

Capen1 demonstrated over 25 years ago that humans do not estimate uncertainty well. He cited experiments that he and other researchers conducted that indicate an almost universal tendency to underestimate uncertainty, often substantially. When asked to provide probability intervals for answers to questions for which they had a wide range of knowledge, people consistently failed to make the probability intervals large enough. This was true of highly technical people, and was true even when they had been told previously that their probability ranges were typically too small. Capen attributed our tendency to underestimate uncertainty in large part to failure to take into account unknown information. We are often able to take into account the uncertainties in information, even fuzzy information, of which we are aware. We are unable, or unwilling, to take into account uncertainties in things for which we have little or no information. Putting people together into a company does not seem to improve their ability to estimate uncertainty. One can easily create a long list of petroleum projects that failed, or at least failed to make profitability targets, due to poor estimation of uncertainty.

Increased attention in the petroleum literature in the last several years indicates an increased recognition of the problem, and the need for increased efforts to model uncertainty more accurately. These are good signs. However, we have seen no evidence to suggest that, as an industry, we have improved significantly in our ability to estimate uncertainty over the last 25 years.