2025-12-11 –, Machine Learning & AI
In this presentation, we demonstrate how Large Language Models (LLMs) can revolutionize safety log analysis in the oil and gas industry. Our research with a major operator involved processing 15,000 safety observations through a novel multi-stage pipeline. First, we developed a domain-specific categorical framework aligned with industry standards. We then implemented an unsupervised learning approach using sentence transformers to calculate semantic similarity between observations and predefined categories. This enabled multi-dimensional classification with weighted confidence percentages. Finally, we deployed a fine-tuned LLM to assign priority scores and enhance categorization accuracy, all while maintaining data privacy through on-premises processing. The resulting system streamlines real-time safety log processing, enabling more efficient identification of potential hazards and trends. Our implementation demonstrates significant improvements in classification accuracy and processing efficiency compared to traditional methods, providing actionable insights for proactive safety management.
This presentation explores a new application of Large Language Models (LLMs) in the oil and gas industry, specifically for safety log analysis. While oil and gas operators have traditionally been cautious in adopting LLM technologies, this project demonstrates a compelling use case that delivers tangible value through enhanced hazard identification and trend analysis. Attendees will learn how our multi-stage LLM pipeline processes safety observations to generate actionable insights while maintaining data privacy through on-premises processing. The presentation will showcase how this approach significantly improves classification accuracy and processing efficiency compared to traditional methods, providing a practical framework for organizations looking to leverage AI for safety management.
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Andrew Yule is a co-founder and managing partner of Pontem Analytics, a global consulting company in the energy industry specializing in combining domain expertise with data-driven solutions. Andrew has 14 years of experience in the energy industry, where he has contributed to a diverse range of projects spanning both offshore and onshore. He has been a member of SPE since he began his career in 2011 and is currently a contributor for SPE’s The Way Ahead magazine as well as a chairman on the Fort Worth SPE board. He is also a member of the Young Entrepreneurial Council. His technical background includes a bachelor’s degree in Chemical Engineering from the Colorado School of Mines and a master’s degree in data science from Southern Methodist University.
Iain Docherty is a Chemical Engineer with over 10 years of experience across nuclear, energy, mining, and renewables sectors. He is currently a Lead Engineer at Pontem Analytics, specializing in combining first-principles modelling with data-driven approaches to optimise processes. Proven experience in developing and deploying control and optimization solutions leveraging deep reinforcement learning and machine learning techniques.