Optimization of Injection Parameters for Polymer-Surfactant Flooding in Low-Permeability Reservoirs Based on an improved PSO Algorithm

Abstract

Polymer-surfactant binary flooding, as a core technical system in chemical flooding, has achieved breakthrough application results in heterogeneous reservoir development through synergistic mechanisms of mobility control and interfacial tension reduction. However, the significant nonlinear correlation between technical-economic indicators poses challenges for conventional experience-driven parameter optimization methods to overcome local optimum constraints, which has become a critical bottleneck restricting industrial-scale implementation. Addressing these challenges, this study establishes a collaborative optimization framework integrating reservoir engineering, numerical simulation, and intelligent algorithms. Initially, a refined reservoir numerical model incorporating polymer viscosity-concentration effects and surfactant adsorption characteristics was developed. Subsequently, an economic evaluation indicator for polymer-surfactant binary flooding (PSF) was established. Ultimately, the improved particle swarm optimization algorithm was implemented for global solution space optimization. Results demonstrate that optimized solutions exhibit remarkable technical-economic synergy. Compared with conventional empirical approaches, the optimized scheme achieves substantial improvements in recovery factor and net present value, respectively, while reducing chemical agent consumption. This research overcomes the local optimization constraints inherent in traditional single-factor analysis methods, significantly enhances optimization efficiency, and provides both theoretical foundation and engineering practice references for intelligent decision-making transformation in chemical enhanced oil recovery technologies.