Frequentist history matching with interval predictor models

Jonathan Sadeghi, Marco De Angelis, Edoardo Patelli

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)29-48
Number of pages20
JournalApplied Mathematical Modelling
Volume61
Early online date17 Apr 2018
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • interval predictor models
  • history matching
  • surrogate model
  • inverse problem
  • imprecise probability
  • frequentist inference

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