A Large Language Model Framework for Risk Cause Interpretation and Mitigation Recommendation in Drilling Operations

Abstract

Summary When drilling risks such as kick events occur, rapidly identifying the root causes and recommending effective mitigation measures is critical for well control and operational safety. Traditional incident analysis mainly relies on expert experience and rule-based diagnostics, which are time-consuming and often lack systematic interpretability. This paper proposes a Large Language Model (LLM)-based framework to automatically interpret risk causes and generate mitigation recommendations following drilling risk events. Historical drilling incident reports, operational logs, engineering guidelines, and mitigation case records are first collected to construct a drilling risk knowledge base. A word embedding model is then domain-fine-tuned using drilling-related corpora to enhance semantic representation of domain-specific terminology and operational context. Based on the enhanced embeddings, a knowledge retrieval mechanism identifies relevant historical cases and engineering knowledge. The retrieved knowledge and event descriptions—including drilling parameters and operational records—are provided to a Large Language Model, which performs structured reasoning to interpret possible causes and generate recommended mitigation measures. The proposed framework is evaluated using drilling incident datasets covering 45 wells and 56 recorded risk cases from wells drilled in western China oilfields, together with associated drilling reports and operational documentation. Experimental results show that domain-fine-tuned word embeddings significantly improve semantic retrieval performance, increasing relevant case retrieval accuracy from 0.713 to 0.863. With the enhanced knowledge retrieval, the LLM can effectively identify key contributing factors and produce structured explanations of incident causation. The system achieves an overall root-cause interpretation accuracy (RCA-Acc) of 0.864 and generates actionable mitigation recommendations such as adjusting drilling fluid density, optimizing circulation parameters, and strengthening well control monitoring procedures. In addition, the framework provides interpretable reasoning outputs that connect operational conditions, risk indicators, and recommended responses. These results demonstrate that the proposed approach can significantly improve the efficiency and consistency of drilling incident analysis while providing practical decision support for field engineers. This study introduces a domain-adapted LLM framework for drilling risk analysis that integrates domain-fine-tuned word embeddings, knowledge retrieval, and large-model reasoning. The proposed approach enables automated cause interpretation and mitigation recommendation after drilling risk events, providing a new paradigm for intelligent incident analysis and decision support in drilling engineering.