Optimization of Sliding Sleeve Opening in Horizontal Well Completion Using an Improved MADDPG Algorithm

Optimization of sliding sleeve completion system involves complex problems such as multivariable, strongly coupled, and nonlinear characteristics. This study establishes a coupled flow productivity model for horizontal well sliding sleeve and reservoir, with the discrepancy between theoretical predictions and numerical simulation results validated to be within 12%. Based on this model, a reinforcement learning framework guided by Markov decision theory is adopted, and a PER-MADDPG algorithm is proposed for optimizing multi-stage sliding sleeve completion in horizontal wells by introducing a prioritized experience replay mechanism. Results show that compared with DDPG and MADDPG, the PER-MADDPG-optimized horizontal well sliding sleeve completion achieves the highest reward with minimal loss during training, increasing cumulative oil production by 10.7% and reducing comprehensive water cut by 6.4% after two years. By intelligently prioritizing and replaying experiences, this algorithm significantly enhances the learning efficiency and sample utilization of agents, providing theoretical guidance for real-time multi-agent control during oil well production.