Frequentist history matching with interval predictor models
Sadeghi, Jonathan and Angelis, Marco De and Patelli, Edoardo (2018) Frequentist history matching with interval predictor models. Applied Mathematical Modelling, 61. pp. 29-48. ISSN 0307-904X
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Abstract
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.
Creators(s): |
Sadeghi, Jonathan, Angelis, Marco De and Patelli, Edoardo ![]() | Item type: | Article |
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ID code: | 70368 |
Keywords: | interval predictor models, history matching, surrogate model, inverse problem, imprecise probability, frequentist inference, Mathematics, Modelling and Simulation, Applied Mathematics |
Subjects: | Science > Mathematics |
Department: | Faculty of Engineering > Civil and Environmental Engineering |
Depositing user: | Pure Administrator |
Date deposited: | 31 Oct 2019 11:57 |
Last modified: | 14 Feb 2021 02:32 |
URI: | https://strathprints.strath.ac.uk/id/eprint/70368 |
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