Hybridization of the Probability Perturbation Method with Gradient Information

Geostatistically based history matching methods make it possible to formulate history matching strategies which will honor geologic knowledge about the reservoir. However, the performance of these methods is known to be impeded by slow convergence rates resulting from the stochastic nature of the algorithm. On the other hand, history matching based on classic gradient-based techniques are under certain circumstances very efficient. However, integration of diverse information about the reservoir geology is not trivial. We will present a hybridized version of the probability perturbation method (PPM) which makes use of qualitative gradient information in order to improve convergence. The resulting method can be applied to continuous properties as well as discrete variables. The proposed algorithm seeks to improve the convergence of traditional PPM by integrating qualitative gradient information. The algorithm is applied to a synthetic reservoir case where multiple point statistics play an important role. The benefit from the inclusion of gradient information is investigated and the results indicate a significant improvement of convergence.<br><br>