Collaborative optimization of well operations and adjustment strategies in waterflooding reservoirs using an enhanced adaptive differential evolution algorithm

Efficient optimization of well operations and adjustment strategies in large-scale waterflooding reservoirs is a high-dimensional and complex challenge due to strong decision coupling and reservoir heterogeneity. This study proposes a collaborative optimization framework that integrates multiple adjustment strategies, including infill well drilling, shut-in of low-efficiency wells, and injection-production well conversion. A penalty mechanism is introduced to balance cumulative oil production maximization with minimum production constraints for infill wells. The core contribution is the development of a multi-strategy enhanced adaptive differential evolution algorithm (E-ADE), which incorporates the follower update mechanism of the Sparrow Search Algorithm (SSA) and the logarithmic spiral search strategy of the Whale Optimization Algorithm (WOA) into the differential evolution (DE) framework. By dynamically adjusting differential evolution vectors and adaptively regulating population size across optimization stages, E-ADE effectively balances global exploration and local exploitation, leading to significantly improved convergence speed and optimization accuracy. Benchmark tests on nine multimodal functions demonstrate that E-ADE consistently outperforms classical algorithms, including DE, GA, PSO, WOA, and SSA. The method is further applied to the PUNQ-S3 reservoir model and the S4 block of the W12-2 oilfield under high water-cut conditions. The results indicate that E-ADE enables adaptive optimization of infill well placement, shut-in schemes, and well-type conversions, achieving coordinated improvements in both field-scale production and single-well performance, and substantially enhancing the efficiency of waterflooding development.