A Robust Optimization Tool Based on Stochastic Optimization Methods for Waterflooding Project

Abstract The increasing demand of crude oil has led to the increasing needs to improve recovery. Waterflooding is one of the most common secondary recovery techniques applied to many reservoirs. The method of waterflooding is to inject water into the reservoir to maintain reservoir pressure. In general, the determination of well locations is structured to form certain pattern (e.g. five spot). However, it is not the only factor affecting the optimum configuration. Besides reservoir rocks, fluid characteristics and well configurations, injection and production strategies (e.g. rates or bottom hole pressures) can also significantly affecting the overall recovery. As reported by other researchers, due to the complex nature of reservoir characterizations and fluid properties, the optimization solution of those parameters tends to have many best possible solutions. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were chosen because their robustness to avoid local optima and finding the global optima of an objective function. The objective function used to drive the solutions were Recovery Factor (RF) and Net Present Value (NPV) of a waterflooding project. An Optimization tool, (OpTool) was developed to prove that stochastic optimization methods could fit best in optimizing the waterflooding project. This tool is capable to communicate with reservoir simulators with known input/output formats to populate cases that would be evaluated using the well-defined objective functions. A synthetic case was built to show the OpTool capabilities to tackle waterflooding optimization problems. This tool is capable to determine an optimum pattern design (e.g. well spacing) and production and injection strategy to maximizing the RF and NPV of the project.