An Efficient Surrogate-Assisted Hybrid Optimization Framework for Polymer Flooding Management

Reservoir injection-production optimization plays a critical role in enhancing recovery in high-water-cut oilfields. While polymer flooding improves development efficiency by increasing viscosity and controlling fluid flow, its optimization faces several challenges: Conventional numerical simulations are computationally expensive, often requiring several hours per run, which severely limits scalability. The incorporation of slug measures introduces high-dimensional coupled modeling difficulties, as discrete variables (e.g., polymer injection timing) must be integrated with continuous variables (e.g., concentration and injection rate). Moreover, single-stage optimization frameworks are prone to converge to local optima and often fail to adapt to the dynamic characteristics of heterogeneous reservoirs. To address these issues, this study proposes a systematic solution framework: (1) A hybrid variable modeling approach is developed, which employs binary encoding to represent slug timing and a radial basis function (RBF) surrogate model based on Gower distance to map discrete-continuous variable relationships; (2) A two-stage surrogate-driven optimization architecture is introduced, where a global phase uses a dual-mode surrogate search to identify high-potential regions, followed by a local refinement phase for continuous parameter adjustment. An adaptive switching mechanism is incorporated to balance exploration and exploitation. Experimental results demonstrate that the proposed Two-Stage Collaborative Optimization (TSCO) framework outperforms state-of-the-art Surrogate-Assisted Evolutionary Algorithms (SAEAs) on multiple benchmark problems.