Autonomous Optimization of Polymer-Enhanced Fluid (PEF) Injection Strategies for Shale Gas Recovery via Multi-Modal Data Fusion and Reinforcement Learning

**Abstract:** The efficient and environmentally responsible extraction of shale gas reserves necessitates sophisticated reservoir management strategies. This research investigates an autonomous framework for optimizing Polymer-Enhanced Fluid (PEF) injection protocols, leveraging advancements in multi-modal data fusion, reinforcement learning (RL), and reservoir simulation. Focusing on minimizing water consumption and maximizing gas recovery, our system dynamically adjusts PEF formulations and injection rates based on real-time reservoir response data. This hybrid approach combines physical modeling, machine learning, and novel scoring metrics, yielding a commercially viable solution for enhanced shale gas recovery with reduced environmental impact.