Study on optimization and numerical simulation of reservoir profile control parameters based on intelligent optimization algorithm

Traditional reservoir profile control methods rely on empirical formulas and trial-and-error methods, and it is difficult to achieve the global optimal solution. Therefore, this paper introduces genetic algorithm (GA) and combines with numerical simulation technology to optimize profile control parameters, including profile control well location, profile control agent type and dosage. By constructing reservoir seepage equation and mass conservation equation, a high-resolution reservoir numerical simulation model is established, and the fitness function is adjusted by penalty function method to realize multi-objective optimization of maximizing oil recovery and minimizing profile control cost. Three schemes are designed in the experiment: benchmark scheme (conventional water injection development), random profile control scheme and profile control scheme based on GA optimization. The results show that the recovery ratio of GA optimized scheme is increased by 10.2 percentage points to 42.7%, the water cut is reduced to 72.3%, and the profile control cost is reduced by 9.5% to 3.8 million yuan. There is a nonlinear relationship between the dosage of profile control agent and oil recovery. When the dosage exceeds 2500 tons, the cost will increase significantly, while nanoparticles (type C) as profile control agent show higher oil recovery growth potential. The research proves that the optimization of reservoir profile control parameters based on intelligent optimization algorithm can significantly improve the reservoir development effect and provide a new technical approach for efficient development and sustainable utilization of oil fields.