Exploring the Application of Prompt Learning for Drilling Risk Identification and Reasoning with Large Language Models

Timely identification of potential hazards and provision of effective mitigation strategies are crucial for ensuring operational safety in deep, ultra-deep, and high-risk drilling operations. To address the limitations of traditional approaches in drilling risk management, this study proposes an intelligent risk identification method based on prompt learning. Real-world drilling risk description data are organized, and various prompt templates are designed to guide a large language model in performing risk type classification and basic causal reasoning without fine-tuning. A test set consisting of typical drilling risk scenarios is constructed, and the impact of different prompting strategies on model output quality is compared. Expert-based manual validation is employed to evaluate the accuracy and reasoning quality of the outputs. Experimental results demonstrate that well-designed prompts can significantly enhance the performance of large language models in drilling risk identification tasks. This study explores the potential of prompt learning to enhance decision support for drilling risk management, providing a new direction for intelligent drilling operations.