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

[thumbnail of Sadeghi-etal-AMM2018-Frequentist-history-matching-interval-predictor-models]
Preview
 Text (Sadeghi-etal-AMM2018-Frequentist-history-matching-interval-predictor-models)
Sadeghi_etal_AMM2018_Frequentist_history_matching_interval_predictor_models.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (17MB)| Preview

    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 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:14 Feb 2021 02:32
      URI:https://strathprints.strath.ac.uk/id/eprint/70368
    CORE (COnnecting REpositories)