Bridging and Unlocking Insights from Drilling Data with Generative AI

Abstract The oil and gas industry is undergoing a digital transformation where artificial intelligence (AI) is becoming central to improving efficiency, safety, and sustainability. With drilling operations producing massive volumes of both structured and unstructured data from real-time sensor readings to daily drilling reports extracting timely, actionable insights remain a key challenge. This paper presents a Generative AI-based solution developed to bridge this gap by transforming how drilling data is analyzed and interpreted. Integrated within a broader AI platform, the solution leverages advanced Large Language Models (LLMs) to process and synthesize structured datasets alongside text-based operational documents. The solution focuses on addressing critical drilling challenges such as lost circulation, stuck pipe, mud and cement control, and non-productive time (NPT). By providing proactive insights and predictive guidance, the system empowers engineers and drilling teams to make faster, smarter, and more sustainable decisions across the well lifecycle. In addition to its predictive and diagnostic capabilities, the proposed system also plays a pivotal role in knowledge retention and continuous learning across drilling projects. By automatically capturing insights, lessons learned, and operational best practices from previous wells, it enables organizations to build a centralized, evolving knowledge base that supports decision-making in future operations. This not only reduces dependency on individual expertise but also ensures consistency in operational performance across teams and geographies. As AI continues to mature, such intelligent solutions will define a new era of cognitive drilling operations, where intelligent systems augment human expertise to achieve safer, faster, and more cost-effective wells.