Intelligent Management of Fracturing Knowledge in Oil and Gas Fields: A Novel Transformation Approach from Document Governance to Natural Language Q&A

Digitalization and intelligence have become pivotal in the realm of oil and gas field development, concomitant with a marked increase in the volume of fracturing-related documentation, the majority of which exists in unstructured form. This has given rise to inefficiencies in data governance and knowledge utilization. To address this, the paper proposes an integrated technical approach of “document governance—knowledge extraction—intelligent Q&A.” We first achieve document standardization through multi-format document parsing and semantic segmentation. Subsequently, by leveraging a knowledge dictionary and the Qwen3-14B (Q8 precision) large language model, the accuracy of professional information extraction is enhanced via keyword positioning and dynamic prompt strategies, achieving an extraction accuracy of 95% and a recall rate of 80%. This provides data and knowledge support for building a structured database and a vector knowledge base. Finally, a natural language Q&A system based on a structured query language agent (SQLAgent) is developed, achieving a Q&A accuracy of over 92%, a response time of less than 30 s, and support for interactive querying of complex questions. We offer a practical technical pathway for the intelligent management and knowledge services of fracturing engineering documents.