AI Driven Knowledge Management in Oil and Gas: A Large Language Model Approach to Operational Excellence

Abstract This paper aims to develop a large language model (LLM)-based expert system to streamline knowledge management in oil and gas operations. By post-training domain-specific data (e.g., engineering protocols, safety guidelines, historical case data), the system functions as a 24/7 virtual assistant, providing accurate operational guidance, statistical analysis, and decision support. The scope covers model architecture design, validation in field operations, and quantification of efficiency gains for operators, targeting a 30% reduction in information retrieval errors and 50% faster access to technical knowledge. The study fine-tunes a foundational LLM (Deepseek or LLaMA) using a curated corpus of oil and gas technical documents, including drilling reports, equipment manuals, and regulatory standards. Post-training incorporates Reinforcement Learning from Human Feedback (RLHF) to align outputs with industry jargon and safety-critical precision. The system deploys via a cloud-edge hybrid platform, enabling real-time Q&A through natural language interfaces. Validation involves A/B testing with 50 field engineers comparing traditional documentation searches against the AI assistant's performance in accuracy (measured by expert review) and time efficiency. Comparation testing of the AI-powered expert system demonstrated transformative improvements in oil and gas operational management. The system reduced 20 minutes on average query resolution time, while achieving 96% answer accuracy compared to 80% for conventional approaches. Notably, the technology contributed to a 70% reduction in procedural errors during critical well interventions by providing context-aware guidance, such as precise chemical dosage recommendations. The AI assistant proved particularly valuable in democratizing knowledge, enabling junior engineers to achieve task competency 80% faster through interactive, step-by-step troubleshooting protocols. While initial testing revealed occasional model hallucinations in rare equipment failure scenarios, this was effectively mitigated through implementation of a confidence-scoring mechanism that flags uncertain responses for human review. The system's ability to instantly retrieve and synthesize information from vast technical databases has significantly reduced reliance on fragmented documentation and subject matter expert availability. These results confirm that properly trained domain-specific LLMs can serve as reliable virtual assistants in high-stakes oilfield operations. Looking ahead, further development will focus on expanding the system's capabilities to interpret technical diagrams and integrate real-time sensor data, paving the way for predictive maintenance and enhanced decision-support functionality. The success of this implementation suggests substantial potential for AI-driven knowledge management to revolutionize operational efficiency and safety standards across the energy sector. This study presents the first LLM application fine-tuned specifically for oil and gas technical operations, bridging gaps in traditional knowledge management. Unlike generic chatbots, the system's post-training on domain data ensures compliance with industry standards while offering auditable response sources. For engineers, this translates to reliable, on-demand expertise—critical in high-risk environments where outdated or incomplete information carries severe HSE consequences.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.