Modular Framework Integrating Large Language Models with Drilling Hazard Detection Systems to Provide Operational Context-Informed Interpretations and Recommended Actions

A modular framework that pairs large language models (via retrieval-augmented generation) with a stuck-pipe detection system to interpret risks and recommend mitigating actions.

SPE-227906-MS — SPE Annual Technical Conference and Exhibition (ATCE), Houston, Texas, USA, October 20, 2025

S. Suhail (Exebenus); T. S. Robinson (Exebenus); O. E. Revheim (Exebenus); P. Bekkeheien (Exebenus)

DOI: 10.2118/227906-MS

Abstract

Large Language Models (LLMs) have emerged as transformative Artificial Intelligence tools for advancing how computers process and generate text data, enabling improvements in applications ranging from natural language understanding and translation, to summarization, content creation and decision support. This work describes a modular framework integrating open-source LLMs with drilling hazard detection systems that generate early warnings, in order to provide interpretations and recommendations for mitigating risks, informed by context programmatically retrieved from external knowledge bases. The developed solution connects an existing stuck pipe risk detection system (OTC-32169-MS, SPE-217963-MS) to a module responsible for summarization of observed risks, generating interpretations of the likely sticking mechanism, and recommending mitigating actions according to best practices, in real-time. Interpretations and recommendations are generated by an LLM utilizing Retrieval Augmented Generation (RAG) to obtain relevant contextual information from a vectorized document collection forming an expert knowledge base.