Optimization for alkali/polymer flooding based on parallel self-regulation differential evolution with maximum average entropy

The alkali/polymer (AP) flooding is a complex distributed parameter system (DPS). In this paper, an optimization model of AP flooding is developed, which takes net present value (NPV) as the performance index, oil/water seepage continuity equations and adsorption diffusion equations of displacing agents as the governing equations, physicochemical algebraic equations and boundary conditions of displacing agents as the constraint equations. To get the optimal injection-production strategy, a parallel self-regulation differential evolution algorithm with maximum average entropy (PSEDE) is proposed. In PSEDE, the maximum average entropy initialization strategy and multi-population parallel strategy are introduced. A new self-regulation principle containing the global population information and individual information is adopted to improve the performance of differential evolution (DE). After tested by three benchmark functions, the PSEDE is applied to optimize an optimization model of AP flooding. The solving effect is good by the comparison with trial and error solutions.