Smart-Well Production Optimization Using an Ensemble-Based Method

Abstract

Summary Ensemble methods have been applied successfully in assisted history matching and in production optimization. In history matching, the ensemble Kalman filter (EnKF) has been used to estimate the values of hundreds of thousands of variables from various types of data. In production optimization, an ensemble-based method has been used to estimate optimal control settings for problems with thousands of control variables. In both cases, relatively small numbers of random realizations are used to compute update directions for improving estimates. In this paper, we illustrate the application of the ensemble-based optimization on two fairly complex problems that would be difficult to handle by other methods. In the first example, we show its application to optimize inflow-control-valve (ICV) settings on two horizontal wells in a sector model of 200,000 cells. One hundred layers were used in the reservoir model to capture geological heterogeneity. The two wells were drilled parallel to the edgewater boundary. The optimization objective in this example is to minimize cumulative water production over a 10-year production period while maintaining a constant liquid-production rate. Results after only five optimization iterations with improved control-valve settings showed a 50% reduction in cumulative water production. The fully automated optimization process was completed within a few hours under a parallel-computing environment. The ensemble-based method was also applied successfully to a 3D case consisting of 10 multilateral wells with ICVs installed at each lateral junction. The interaction of various laterals is difficult to visualize, but the optimization algorithm was again successful in reducing water production. In this example, we demonstrate that proper choice of control variables can be important to the success of the optimization.