Recently, Zhang Kai’s team made new progress in the field of oil and gas engineering knowledge intelligence and large-model intelligent agent applications. The research results were published in Drilling and Production Technology, with the paper titled “Construction and Application of a Large-Model Intelligent Agent for Knowledge Extraction, Integration, and Generation in the Oil and Gas Domain.”
Innovation: Existing applications of large models in the oil and gas domain mainly focus on data retrieval, knowledge question answering, and general text generation. However, engineering tasks such as downhole operation design involve the correlation of multi-source data, professional parameter constraints, and standardized process control. Relying solely on general-purpose large models makes it difficult to ensure the accuracy and consistency of generated results. To address these issues, this study integrates knowledge extraction, knowledge graphs, RAG/GraphRAG, and intelligent agent technologies, and proposes a large-model intelligent agent construction method for knowledge extraction, integration, and generation in oil and gas engineering. The method transforms multi-source information, such as well history data, operation documents, production dynamics, and technical standards, into a retrievable and inferable domain knowledge base. It then uses intelligent agents to complete data retrieval, design generation, parameter verification, and result optimization. The results show that this method can effectively improve the preparation efficiency, section completeness, and key-parameter consistency of the three-part pump-inspection operation design, demonstrating good application performance in intelligent oil and gas engineering design.
Abstract: As oil and gas exploration and development continue to move toward deeper formations, unconventional resources, and complex operating conditions, well engineering practices face higher requirements for knowledge acquisition efficiency, decision timeliness, and compliance. Traditional approaches that rely on manual document review and experience transfer are increasingly inadequate for rapid responses in knowledge-intensive tasks. To address the multi-source heterogeneity, complex structure, and frequent updates of oil and gas engineering documents, this study proposes a large-model-based agent construction scheme that integrates knowledge extraction, knowledge integration, and knowledge generation. At the data layer, the system consolidates well history records, operation documents, and production dynamics. At the knowledge engineering layer, it builds an oil and gas engineering knowledge graph and a semantic vector store through entity-relation-event extraction, multi-source alignment, and quality governance. At the knowledge-augmented generation layer, RAG and GraphRAG are combined to explicitly inject domain knowledge into the reasoning and generation process of the large language model. At the agent and application layer, memory, planning, and tool-calling mechanisms are used to package knowledge capabilities into executable workflows for specific tasks. Using rod-pumped well pump-pulling operation design as a case study, an integrated agent system covering geological design, engineering design, and construction design was developed and piloted. Results show that, without altering the existing technical management framework, the proposed approach significantly shortens the turnaround time for the three-part design, improves document section completeness and key-parameter consistency, and achieves high subjective satisfaction among engineers, demonstrating the feasibility and practical value of integrated extraction-integration-generation LLM agents in oil and gas engineering applications.
Drilling and Production Technology covers the fields of oil and gas drilling, completion, oil and gas production, workover, stimulation, oilfield chemistry, drilling and production equipment, and production management, including drilling technology, production technology, workover technology, oil and gas field development, oil and gas production engineering, drilling and production machinery, oilfield chemistry, reservoir stimulation, downhole operations, production performance analysis, intelligent oilfields, oil and gas artificial intelligence, and special robotics. Founded in 1978, the journal has the Chinese standard serial number CN 51-1177/TE and the international standard serial number ISSN 1006-768X. It is a bimonthly journal and an important scientific and technical journal in the field of oil and gas drilling and production engineering in China. The journal is included in A Guide to the Core Journals of China compiled by Peking University and is recognized as a Chinese core journal. According to the journal statistics page of Wanfang Data, Drilling and Production Technology has a CSTPCD impact factor of 2.221 and an average citation rate of 8.72 per article. It has strong industry influence in technical exchange and achievement promotion in oil and gas drilling and production engineering.
Paper link:
https://doi.org/10.3969/j.issn.1006-768X.2026.01.03
Citation:
Zhang K, Zhang B B, Zhang L M, et al. Construction and Application of a Large-Model Intelligent Agent for Knowledge Extraction, Integration, and Generation in the Oil and Gas Domain[J]. Drilling and Production Technology, 2026, 49(1): 24-34.
