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.

ORCID iDs

Sadeghi, Jonathan, Angelis, Marco De ORCID logoORCID: https://orcid.org/0000-0001-8851-023X and Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
  • Item type:Article
    ID code:70368
    Dates:
    Date
    Event
    1 September 2018
    Published
    17 April 2018
    Published Online
    5 April 2018
    Accepted
    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:22 Mar 2023 02:13
    URI:https://strathprints.strath.ac.uk/id/eprint/70368
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