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 ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
    Item type:Article
    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:27 May 2020 01:01
    URI:https://strathprints.strath.ac.uk/id/eprint/70368
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