Preliminary Development of an AI-Enhanced Multimodal Analysis Framework for Drilling Operations

Abstract Non-Productive Time (NPT) in drilling operations, particularly from events like stuck pipe, incurs substantial financial losses. Traditional mitigation strategies are often reactive and struggle with the dynamic nature of modern drilling and the overwhelming volume of disparate data sources. This paper introduces a novel multimodal AI supervisor designed to proactively reduce NPT and enhance operational efficiency. The proposed system architecture continuously ingests and integrates heterogeneous data like: structured logs, unstructured daily reports and procedures, real-time sensor telemetry, camera feeds, and audio streams, converting them into joint operational embeddings. Real-time risk assessment is performed by comparing the current operational embedding against a comprehensive vector database of historical NPT incidents and optimal drilling states. Upon detecting elevated risk, a Large Language Model (LLM) agent, leveraging Retrieval – Augmented Generation (RAG), automatically retrieves and presents company-approved procedural guidelines from vetted manuals to rig crews, steering them away from likely NPT scenarios.