Frequentist history matching with interval predictor models

Tools

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)

[thumbnail of Sadeghi-etal-AMM2018-Frequentist-history-matching-interval-predictor-models]
Preview
 Text. Filename: 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 ORCID logoORCID: https://orcid.org/0000-0001-8851-023X and Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
CORE (COnnecting REpositories)