Stochastic simplex approximation gradient for reservoir production optimization: Algorithm testing and parameter analysis

Production optimization is an effective technique to maximize the oil recovery or the net present value in reservoir development. Recently, the stochastic simplex approximation gradient (StoSAG) optimization algorithm draws significant attention in the optimization algorithm family. It shows high searching quality in largescale engineering problems. However, its optimization performance and features are not fully understood. This study evaluated and analyzed the influence of some key parameters related to the optimization process of StoSAG including the ensemble size to estimate the approximation gradient, the step size, the cut number, the perturbation size, and the initial position by using 47 mathematical benchmark functions. Statistical analysis was employed to diminish the randomness of the algorithm. The quality of the optimization results, the convergence, and the computational time consuming were analyzed and compared. The parameter selection strategy was presented. The results showed that a larger ensemble size was not always favorable to obtain better optimization results. The increase of the search step size was favorable to escape from the local optimum. A large step size needed to match a large cut number. The increase of cut number was beneficial to increase the local searchability, but also made the algorithm more easily fall into the local optimum. The random initial position was beneficial to find the global optimal point. Moreover, the effectiveness of the parameter selection strategy was tested by a classical reservoir production optimization example. The final net present value (NPV) for water flooding reservoir production optimization substantially increased, which indicated the excellent performance of StoSAG by adjusting the key parameters.

Automated summary

The study evaluated the impact of key parameters in the StoSAG optimization process, including ensemble size, step size, cut number, perturbation size, and initial position. Results showed that larger ensemble size and increased search step size were favorable for optimization results, but a large step size needed to match a larger cut number. Moreover, the increase of cut number was beneficial for local searchability but also increased the risk of falling into local optima. Random initial position was found to be helpful in finding the global optimal point.