Identifiability of model discrepancy parameters in history matching

Muzammil H. Rammay, Ahmed H. Elsheikh, Yan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


In this work, we investigate different approaches for history matching of imperfect reservoir models while accounting for model error. The first approach (base case scenario) relies on direct Bayesian inversion using iterative ensemble smoothing with annealing schedules without accounting for model error. In the second approach the residual, obtained after calibration, is used to iteratively update the covariance matrix of the total error, that is a combination of model error and data error. In the third approach, PCA-based error model is used to represent the model discrepancy during history matching. However, the prior for the PCA weights is quite subjective and is generally hard to define. Here the prior statistics of model error parameters are estimated using pairs of accurate and inaccurate models. The fourth approach, inspired from Köpke et al. (2017), relies on building an orthogonal basis for the error model misfit component, which is obtained from difference between PCA-based error model and corresponding actual realizations of prior error. The fifth approach is similar to third approach, however the additional covariance matrix of error model misfit is also computed from the prior model error statistics and added into the covariance matrix of the measurement error. The sixth approach, inspired from Oliver and Alfonzo (2018), is the combination of second and third approach, i.e. PCA-based error model is used along with the iterative update of the covariance matrix of the total error during history matching. Based on the results, we conclude that a good parameterization of the error model is needed in order to obtain good estimate of physical model parameters and to provide better predictions. In this study, the last three approaches (i.e. 4, 5, 6) outperform the others in terms of the quality of the estimated parameters and the prediction accuracy (reliability of the calibrated models).

Original languageEnglish
Title of host publicationSPE Reservoir Simulation Conference 2019
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781613996348
Publication statusPublished - 10 Apr 2019
EventSPE Reservoir Simulation Conference 2019 - Galveston, United States
Duration: 10 Apr 201911 Apr 2019


ConferenceSPE Reservoir Simulation Conference 2019
Abbreviated titleRSC 2019
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Modelling and Simulation


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