Optimization of reservoir well network deployment strategy and construction of dynamic adjustment model integrating reinforcement learning

This article proposes a well network deployment strategy optimization and dynamic adjustment model that integrates reinforcement learning (RL) to address the shortcomings of traditional reservoir well network deployment optimization methods in drawing on historical control experience. The deployment of reservoir well network, as the core link of oilfield development, directly affects the recovery rate and economic benefits of development. However, existing methods generally face problems such as low optimization efficiency and difficulty in effectively utilizing historical regulation experience. Therefore, this article constructs a data-driven RL framework based on historical regulation, which achieves flexible modeling and reward and punishment mechanism design for dynamic development processes through continuous interaction between intelligent agents and reservoir environments, enabling the model to autonomously learn optimal regulation strategies under complex and changing geological and production conditions. This method not only fully excavates effective information from historical regulation experience, but also has the ability to dynamically adjust online, improving the intelligence level of decision-making. The results indicate that our model can automatically achieve collaborative optimization of well layout and production parameters, significantly reducing manual intervention and effectively improving development efficiency and final recovery rate.