Frequentist history matching with Interval Predictor Models

Abstract

• History matching is achieved with limited knowledge regarding the measurement error. • Interval Predictor Models are used to robustly quantify the measurement error. • Bounds are obtained on the p -values from frequentist inference . • The method is applied to an industrial oil reservoir test case model. In this paper a novel approach is presented for history matching models without making assumptions about the measurement error. Interval Predictor Models are used to robustly model the observed data and hence a novel figure of merit is proposed to quantify the quality of matches in a frequentist probabilistic framework . The proposed method yields bounds on the p -values from frequentist inference. The method is first applied to a simple example and then to a realistic case study (the Imperial College Fault Model) in order to evaluate its applicability and efficacy. When there is no modelling error the method identifies a feasible region for the matched parameters, which for our test case contained the truth case. When attempting to match one model to data from a different model, a region close to the truth case was identified. The effect of increasing the number of data points on the history matching is also discussed.