Comparison of Constrained Life-Cycle Production Optimization Algorithms Based on Stochastic Simplex Approximate Gradients

Abstract

Abstract This study focuses on the optimization of net present value (NPV) with respect to well controls such as injection rates and producing bottomhole pressures (BHPs), subject to the nonlinear constraints of field liquid production rate (FLPR) and water rate for producers (WWPR). The core challenge is to maximize NPV while adhering to both linear and nonlinear constraints, crucial for effective reservoir management. The research presents a detailed comparison of three sequential quadratic programming (SQP)-based optimizers—TU-NLCPO, our in-house nonlinear production optimizer, and two publicly available optimizers; namely Sequential Least SQuares Programming optimizer (SLSQP), and Sparse Nonlinear OPTimizer (SNOPT)—highlighting their application in constrained life-cycle production optimization, a key aspect of closed-loop reservoir management. These optimizers are evaluated based on their ability to handle optimization variables like BHPs and well injection rates (WIRs) over fixed control steps. Stochastic simplex approximate gradients (StoSAG) is applied across the three algorithms for a fair comparison. This comparison is performed using the Brugge model for waterflooding, providing a rigorous real-world assessment of each optimizer’s performance. The findings reveal that TU-NLCPO consistently achieves higher NPV compared to SLSQP and SNOPT, indicating its superior optimization capability. Additionally, TU-NLCPO and SLSQP are found to be robust against different initial guesses, demonstrating consistent NPV values, whereas SNOPT shows variability despite reasonable overall performance. When it comes to adhering to nonlinear constraints, SLSQP exhibits superior performance, especially in managing FLPR within set limits. The study also explores the impact of varying the number of perturbations for stochastic gradient generation on optimizer performance, noting an improvement across all methods with more perturbations.