Use of Neuro-Simulation techniques as proxies to reservoir simulator: Application in production history matching

Abstract

The Well Placement Optimization, Field Development Scheduling, History Matching with Multiple Models, Global Optimization of Oil Production Systems are procedures related to Reservoir Engineering realized generally with base in a very large number of reservoir simulations which can yield slow developments and large computational effort. To minimize this problem some techniques such as Spline, Neural Networks, Kriging and Experimental Design, have been presented in the literature to be used as proxies to reservoir simulator. Due to the importance of the decisions related to the development and management of petroleum fields, the development of proxies with high accuracy can be a decisive aspect in a project. The successful applications of Neural Networks in several research fields suggest the investigation of appropriated architectures to be used as proxies to reservoir simulator. In this article novel proxies to reservoir simulator which are based on Neuro-Simulation techniques are introduced that present a high accuracy to reservoir simulator. Five different architectures of Neural Networks were studied and applied in two case studies related to history matching. The results obtained showed the large potential of application of the techniques introduced.