Zero-Shot Learning With Large Language Models Enhances Drilling-Information Retrieval

Finding information across multiple databases, formats, and documents remains a manual job in the drilling industry. Large language models (LLMs) have proven effective in data-aggregation tasks, including answering questions. However, using LLMs for domain-specific factual responses poses a nontrivial challenge. The expert-labor cost for training domain-specific LLMs prohibits niche industries from developing custom questionanswering bots. The complete paper tests several commercial LLMs for information-retrieval tasks for drilling data using zero-shot in-context learning. In addition, the model's calibration is tested with a few-shot multiple-choice drilling questionnaire. © 2025 Society of Petroleum Engineers (SPE). All rights reserved.