A Data-Driven Proxy Modeling Approach Adapted to Well Placement Optimization Problem

Well placement optimization (WPO) plays an essential role in field management and economy. However, it entails massive computational time and demand since hundreds, even thousands, simulation runs are needed. Different types of proxy models have been utilized to address this issue. Among different proxy models, data-driven proxies are preferred as they can determine the combined effect of several parameters without suffering from the type and the number of modeling parameters. This article aims to develop a data-driven proxy model in an artificial intelligence framework adapted to the WPO problem. This proxy estimates and compares the oil recovery for different well configurations. Our contribution is building a dynamic proxy by training a sequence of static proxies in a time-dependent manner to make more benefit from the modeling capability of artificial neural networks (ANNs). The workflow comprises preparing a learning database using experimental design techniques, finding the significant parameters by searching the parameter space, training and validating a series of ANNs to obtain the desired field response, and conducting a blind test to ensure the model performance and generality. This proxy is then coupled with the genetic algorithm to find an optimal well configuration in a test case. Verifying the results obtained by our proxy with those of a commercial simulator shows that the objectives of constructing this proxy for WPO are successfully achieved.