基于强化学习的压裂泵注自主决策模型OA

Existing decision-making methods for fracturing pumping mostly rely heavily on human experience and monitoring data,leading to response lag and uncertainty,so they struggle to fully adapt to the rapid and complex changes during the fracturing process.Focusing on the online optimization of fracturing operations,this paper proposes a deep reinforcement learning-based autonomous decision-making method for fracturing pumping,which perceives fracturing states in real time using agents and autonomously determine pumping rate and proppant concentration.In addition,tests are carried out on 20 fracturing stages not involved in training to verify the generalization capacity of the agent.The following results are obtained.First,compared with other network structures,the GRU+MLP dynamic-static feature fusion network achieves higher average value estimation and more stable convergence.Second,the TD3 algorithm,combined with training mechanisms including experience replay and target network soft update can reduce value-estimation fluctuations and improve strategy optimization stability.Third,in the test fracturing stages,the agent achieves an average response time of less than 0.1 s under a 60 s decision window,increases the conformity rate of fracture half-length from 85.4%to 96.0%and that of fracture width from 77.4%to 82.8%,improves the final reward from 6.28 to 6.66,and weakens wellhead and bottomhole pressure fluctuations.In conclusion,the proposed closed-loop decision-making system based on deep reinforcement learning can achieve real-time autonomous control of fracturing pumping parameters,promote the transformation of fracturing operations from static design driven to dynamic intelligent decision-making,providing a novel technical idea for intelligent fracturing of unconventional reservoirs.