AI Enhanced Predictive Platform for Optimizing Fishing Operations

Fishing operations to retrieve lost or stuck downhole equipment remain a major source of non-productive time (NPT) and operational cost in global oilfields. This paper presents FishSight AI, an intelligent platform that integrates machine learning, real-time drilling parameters, and expert decision systems to optimize fishing tool configuration and Bottom Hole Assembly (BHA) design. By analyzing real-time drilling data, such as torque, weight on bit, rate of penetration, and standpipe pressure, the platform predicts downhole anomalies, recommends context specific BHAs, and suggests tool configurations based on available inventory. FishSight AI includes an expert-guided interface that supports either interaction with a trained AI model (ChatAI) or live consultation with human fishing experts. The platform also features an intuitive drag-and-drop BHA design module, allowing engineers to visually assemble fishing strings by selecting tools from a smart inventory list. As the user builds the BHA, the system provides real-time feedback on success probability, compatibility, and field-proven best practices, helping prevent common design errors and accelerating planning time. This paper outlines the architecture of FishSight AI, the machine learning models employed, field deployment strategies, and a case study series illustrating the platform's versatility. Feedback from field engineers in simulated scenarios is discussed. FishSight AI represents a step toward digitizing fishing operations by combining operational knowledge, predictive analytics, and adaptive user interfaces into a unified decision support system.