Enhancement of SPSA algorithm performance using reservoir quality maps: Application to coupled well placement and control optimization problems

Abstract Determination of optimal well location is a vital step in the design of an efficient field development plan which aims to maximize the hydrocarbon recovery and profits from the project. This step has become further complicated as the well control parameters are also introduced into the problem which is known as coupled well placement and control optimization problem. As the number of optimization variable increases, computational cost also increases and it becomes a serious challenge, especially in high resolution reservoir models since the optimization process becomes very time demanding. Spontaneous Perturbation Stochastic Approximation (SPSA) has been introduced as an efficient algorithm in finding the optimum solution for problems with a large number of variables. In this work, SPSA algorithm has been modified using reservoir quality maps to enhance and accelerate finding the good solutions with a minimum number of function evaluation which is desirable for high-resolution reservoir models and high dimensional optimization problems. In this modification, reservoir quality maps are employed to navigate production wells toward high potential areas instead of random movement in standard SPSA. The modified algorithm is tested for joint well placement and control optimization problem using both simultaneous and sequential optimization approach in a synthetic and a benchmark model. The results show that modified SPSA outperforms standard SPSA both in terms of final result and convergence trend. Also, in most cases, simultaneous well placement and control optimization approach gives better results compared to sequential approaches for both algorithms.