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 language | English |
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Pages (from-to) | 29-48 |
Number of pages | 20 |
Journal | Applied Mathematical Modelling |
Volume | 61 |
Early online date | 17 Apr 2018 |
DOIs | |
Publication status | Published - 1 Sept 2018 |
Keywords
- interval predictor models
- history matching
- surrogate model
- inverse problem
- imprecise probability
- frequentist inference