Optimization Algorithms for Automatic History Matching of Production Data

Within the framework of Bayesian statistics, realizations or estimates of rock property fields can be generated by automatic history matching of production data using a prior model to provide regularization. In this context, automatic history matching requires the minimization of an objective function which includes both model and data mismatch terms. For large scale problems, the computational efficiency and robustness of the optimization algorithms used for minimization are of paramount importance. From a comparison of algorithms for a variety of history matching problems, a scaled limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm was identified as the most promising for large scale optimization problems.

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