Proxy Model Based on Artificial Intelligence Technique for History Matching - Application to Brugge Field

Abstract

Abstract History matching of production data is a process requiring a large number of reservoir simulations that are huge time-consuming. To reduce the computation time, an approach consists of using a meta-model to replace the reservoir simulator. In this paper, a proxy model based on an artificial intelligence technique (artificial neural network) is evaluated and compared to proxy models conventionally used for history matching such as polynomials or kriging methods. The proposed approach provides accurate prediction results to speed up and improve the history matching process. An application to the Brugge field is presented.