Domain Driven Methodology Adopting Generative AI Application in Oil and Gas Drilling Sector

Abstract In dynamic landscape of oil and gas drilling, Generative Artificial Intelligence (Generative AI) emerges as the indispensable ally, leveraging historical drilling data to revolutionize operational efficiency, mitigate risks, and empower informed decision-making. Existing Generative AI methods and tools, such as Large Language Models (LLMs) and agents, require tuning and customization to the oil and gas drilling sector. Applying Generative AI in drilling confronts hurdles such as ensuring data quality and navigating the complexity of operations. A methodology integrating Generative AI into drilling demands is comprehensive and interdisciplinary. Agile strategy revolves around constructing a network of specialized agents of LLMs, meticulously crafted to understand industry-specific terminology and intricate operational relationships rooted in drilling domain expertise. Every agent is linked to manuals, standards, specific operational drilling data source and it has unique instructions optimizing computational efficiency and driving cost savings. Moreover, to ensure cost-effectiveness, LLMs are selectively employed, while repetitive user inquiries are addressed through data retrieval from an aggregated storage. Consistent responses to user queries are provided through text and graphs revealing insights from drilling operations, standards, manuals, practices, and lessons learned. Applied methodology efficiently navigates inside the pre-processed user database relying on custom agents developed. Communication with the user is set in the form of chat framed within a web application, and queries on the database about hundreds of wells are answered in less than a minute. Methodology can analyze data and graphs by comparing Key Performance Indicators (KPIs). A wide range of graph output is represented by bar charts, scatter plots, and maps, including self-explaining charts like Time versus Depth Curve (TVD) with Non-Productive Time (TVD) events marked with details underneath. Understanding the data content, data preparation steps, and user needs is fundamental to a successful methodology application. The proposed Generative AI methodology is not just a tool for data interpretation, but a catalyst for real-time decision-making in complex drilling environments. Its integration into oil and gas drilling operations signifies a pivotal advancement, showcasing its transformative potential in revolutionizing the industry's landscape. This approach leads to notable cost reductions, improved resource utilization, and increased productivity, paving the way for a new era in drilling operations. A method driven by selective, cost-effective, and domain specific LLM agents stands poised to revolutionize drilling operations, seamlessly integrating generative AI to amplify efficiency and propel informed decision-making within the oil and gas drilling sector.