Interpretable Damage Analysis for Fault Diagnosis and Maintenance Sequence Optimization of Artificial Lift Devices

Abstract Artificial lift systems are employed in approximately 95% of global oil wells, yet their operational lifespans are often curtailed by complex geological conditions, equipment fatigue, and production demands. To address these challenges, this study proposes an innovative Interpretable Damage Analysis (InterDA) method for fault diagnosis and maintenance optimization. By combining adaptive temporal feature segmentation with expert rule-based interpretation, InterDA assesses the operational status of equipment and quantifies potential risk sources. A risk score enhancement strategy is further employed to prioritize high-risk wells for maintenance scheduling. Experimental results demonstrate a 100% fault diagnosis accuracy, effectively identifying issues such as insufficient fluid supply, sanding, tubing perforation, and pump failures, while minimizing downtime. Field applications confirm that InterDA significantly improves resource allocation, enhances equipment stability, and boosts overall oilfield productivity. The interpretability and efficiency of InterDA offer a novel approach to intelligent management and optimization of artificial lift systems.