Retrieval-Augmented Generation for Drilling and Completion Knowledge Management

Abstract This paper aims to present the development and implementation of an innovative Generative AI solution designed to improve knowledge management in the petroleum industry. The primary objective is to streamline access to crucial information, transforming vast amounts of raw data into actionable insights to support decision-making in well construction operations. The system employs a structured methodology centered around Retrieval-Augmented Generation (RAG) techniques. The approach involves converting unstructured data into semantic vector representations using embedding models to enable relevant and contextualized searches. The process includes multiple stages, such as defining real-world search scenarios, creating a tailored data pipeline, and validating the system through expert-supervised evaluations. This pipeline delivers an effective consultative tool for human specialists, allowing contextual real-time question answering. The solution has demonstrated improvements in operational efficiency for oil and gas professionals. By replacing traditional, time-intensive information retrieval methods, it enables near-instantaneous access to insights, reducing the time previously spent sifting through extensive documentation. Observations reveal that the system provides accurate, contextually relevant responses, significantly accelerating decision-making processes. Moreover, its unique ability to trace information sources and manage access profiles distinguishes it from traditional RAG systems. This work introduces an innovative combination of Generative AI and RAG tailored specifically for the challenges of the oil and gas industry. The implementation of semantic embeddings for precise contextual search, combined with unique features such as traceability and modular architecture, represents a leap forward in information retrieval methodologies. Beyond its immediate application, the system's adaptability offers a scalable framework for other sectors, positioning it as a transformative knowledge management tool in both energy and broader industries.