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 (https://doi.org/10.1016/j.apm.2018.04.003)
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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.
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Item type: Article ID code: 70368 Dates: DateEvent1 September 2018Published17 April 2018Published Online5 April 2018AcceptedSubjects: Science > Mathematics Department: Faculty of Engineering > Civil and Environmental Engineering Depositing user: Pure Administrator Date deposited: 31 Oct 2019 11:57 Last modified: 09 Apr 2024 00:58 URI: https://strathprints.strath.ac.uk/id/eprint/70368