CNPC Xinjiang Oilfield Deploys First Industrial LLM for Pumping Well Production Optimization

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CNPC Xinjiang Oilfield Deploys First Industrial LLM for Pumping Well Production Optimization

Content

The first industrial large language model for pumping well production optimization in China's oil and gas industry has been operating at scale at CNPC Xinjiang Oilfield since April 12. The model, independently developed by CNPC's Exploration and Development Research Institute in partnership with Xinjiang Oilfield, addresses critical oil production challenges by deeply integrating production data from 78,000 pumping wells across four major oilfields including Daqing, Liaohe, Changqing, and Xinjiang. This has increased data availability from 40% to over 95%. The R&D team employed Transformer architecture and contrastive learning algorithms to train a one-billion-parameter pumping well production optimization large language model, fine-tuning scenario-specific models based on plant requirements. Intelligent fault diagnosis, as the first deployed application scenario, has achieved full coverage of稀油 pumping wells across multiple production plants including the first, second, and Baijkouquan plants. The system uses different colors to indicate well health status, making alarms, early warnings, and problem wells immediately visible. Technicians can click on an abnormal well number to view a comprehensive diagnostic report along with diagnostic conclusions, judgment basis, and disposal recommendations. The system has fundamentally changed the traditional experience-based judgment mode. The number of wells managed per person has increased from 3.5 to 11.5, effectively improving production efficiency and reducing operating costs. The average abnormal condition diagnosis accuracy rate exceeds 90%, and the anomaly discovery cycle has been shortened from day-level to minute-level. Next, the R&D team will continue to focus on making the large model deliver tangible results, continuously iterating and optimizing model capabilities, and conducting embodied AI agent research.