A Novel Streamline-Based Objective Function for Well Placement Optimization in Waterfloods

Abstract

In reservoir development plans, well placement optimization is usually performed to better sweep oil and reduce the amount of trapped oil inside reservoirs. Long-term optimization of well placement requires multiple times simulation of reservoirs which makes these problems cumbersome, especially when a large number of decision variables exist. Cumulative oil production (COP) or net present value (NPV) functions are commonly used as the objective function of optimal enhance oil recovery projects. Use of these functions requires a full-time reservoir simulation and their convergence could be difficult with the chance to be trapped in local optimum solutions. In this study, the novel proportionally distributed streamlines (PDSLs) target function is proposed that can be minimized to reach the optimal well placement. PDSL can be estimated even without full-time reservoir simulation. PDSL tries to direct the appropriate number of streamlines toward the regions with larger amount of oil in the shortest time and hence can improve oil recovery. Particle swarm optimization (PSO) method linked to an in-house streamline-based reservoir simulator is implemented to optimize well placement of water-flooding problems in a two-dimensional heterogeneous reservoir model.