Frequentist history matching with Interval Predictor Models

Sadeghi, Jonathan ORCID: 0000-0003-4106-2374, de Angelis, Marco ORCID: 0000-0001-8851-023X and Patelli, Edoardo ORCID: 0000-0002-5007-7247
(2018) Frequentist history matching with Interval Predictor Models. APPLIED MATHEMATICAL MODELLING, 61. pp. 29-48.

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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.

Item Type: Article
Uncontrolled Keywords: Interval Predictor Model, History matching, Surrogate model, Inverse problem, Imprecise probability, Frequentist inference
Depositing User: Symplectic Admin
Date Deposited: 19 Jun 2018 11:59
Last Modified: 19 Jan 2023 01:32
DOI: 10.1016/j.apm.2018.04.003
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