Comparing Genetic Algorithms and Newton-Like Methods for the Solution of the History Matching Problem

Abstract

Abstract. In this work we presents a comparison of different optimization methods for the automatic history matching problem of reservoir simulation. The history matching process is an inverse problem that searches a set of parameters that minimizes the difference between the model performance and the historical performance of the field. This model validation process is essential and gives credibility to the predictions of the reservoir model. Derivative-based methods are compared to a free-derivative algorithm. In particular, we compare the Quasi-Newton method, non-linear Conjugate-Gradient, Steepest-Descent and a Genetic Algorithm implementation. Several tests are performed and the preliminary results are presented and discussed.