Bayesian-Style History Matching: Another Way To Underestimate Forecast Uncertainty?

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 175121, “Bayesian-Style History Matching: Another Way To Underestimate Forecast Uncertainty?” by Jeroen C. Vink, Guohua Gao, and Chaohui Chen, Shell, prepared for the 2015 SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. The paper has not been peer reviewed. Current theoretical formulations of assisted-history-matching (AHM) problems within the Bayesian framework [e.g., ensemble-Kalmanfilter (EnKF) and randomized-maximum- likelihood (RML) problems] are typically based on the assumption that simulation models can reproduce field data accurately within the measurement error. However, this assumption does not hold for AHM problems of real assets. This paper critically investigates the impact of using realistic, inaccurate simulation models. In particular, it demonstrates the risk of underestimating uncertainty when conditioning real-life models to large numbers of field data. Introduction Improved simulation and history-matching techniques have still not cured the chronic ailment of systematically underestimating uncertainty in forecast results. This is because it is very easy to forget or ignore uncertain factors or model assumptions; also, the method used to constrain a model to historical data may excessively reduce uncertainties. In this paper, the authors highlight the fact that stochastic, Bayesian-style history-matching methods can very easily lead to unrealistically reduced uncertainty ranges in forecast results. The complete paper discusses Bayesian-style history matching and uncertainty quantification in detail. Large-Data Danger Stochastic history matching has two contrasting stages. First, one identifies a large number of uncertain model parameters in order to be confident that future field-production results will be contained within the range of forecast results associated with the prior model uncertainty. Second, when actual field data have become available, one uses large quantities of data to reduce this prior uncertainty. The reservoir engineer must decide not only which model parameters and field data to use, but also how many. The effect of increasing the number of uncertain model parameters is intuitively clear: Every parameter that has an impact on a specific forecast result will add to the uncertainty of this result.