Construction of Inter-Injection and Production Well Network Pumping Control Model Driven by MEA Multi-Objective Intelligent Optimization Algorithm

In view of the optimization challenge of the pumping control problem between injection and production wells in oil and gas field development, traditional methods are difficult to take into account the multi-objective optimization requirements and dynamic adaptability, resulting in limited well network control efficiency and unstable production results. To this end, this paper proposes a pumping control model between injection and production wells based on MEA (Multi-objective Evolutionary Algorithm). First, a multi-objective optimization framework is constructed to ensure the comprehensiveness of the optimization objectives. Then, an intelligent search mechanism is applied to avoid the limitations of traditional methods that are prone to fall into local optimality. Then, the dynamic constraint adjustment mechanism is combined to enable the algorithm to adjust the optimization strategy according to the real-time working conditions. Finally, an intelligent collaborative optimization module is constructed to adjust the inter-well pumping strategy in combination with the dynamic coupling relationship between wells to achieve refined control of the well network operation status. The experimental results show that under the 6 × 6 well pattern model, the recovery factor of the MEA method is increased from 0.3 to 0.65, the unit energy consumption is reduced from 8 to 4 MJ/t, the pressure balance error is reduced from 5 to 0.5 MPa, and 95% of the optimal solutions are achieved within 200 iterations. The optimization effect is significantly better than traditional algorithms such as GA (Genetic Algorithm), PSO (Particle Swarm Optimization) and GD (Gradient Descent). In addition, the MEA algorithm shows strong real-time optimization capabilities in a dynamic reservoir environment, and can quickly adapt to external disturbances such as pressure mutations, fluid viscosity changes and production declines, effectively improving the intelligent development level of oil fields.