A framework to integrate history matching and geostatistical modeling using genetic algorithm and direct search methods

History matching is an inverse problem where the reservoir model is modified in order to reproduce field observed data. Traditional history matching processes are executed separately from the geological and geostatistical modeling stage due to the complexity of each area. Changes made directly on the reservoir properties generally yield inconsistent geological models. This work presents a framework to integrate geostatistical modeling and history matching process, where geostatistical images are treated as matching parameters. The traditional optimization methods normally applied in history matching generally use gradient information. The treatment of geostatistical images as matching parameters is difficult for these methods due to the strong non-linearities in the solution space. Therefore, another objective of this work is to investigate the application of two optimization methods: genetic algorithm and direct search method in the proposed framework. In order to accelerate the optimization process, two additional techniques are used: upscaling and distributed computing. Results are presented showing the viability of the genetic algorithm in the type of problem addressed in this work and also that direct search method can be used with some restriction. Finally, the benefits of distributed computing and the consistence of the upscaling process are shown.