石油科学通报2026，Vol.11Issue(4)：1096-1109,14.DOI:10.3969/j.issn.2096-1693.2026.02.040
基于大模型的油气生产系统完整性管理:现状与挑战
Integrity management of oil and gas production systems based on large models:Current situation and challenges
摘要
Abstract
Integrity management of oil and gas production systems is a core component for ensuring safe equipment operation,environmental compliance,and production continuity throughout the life cycle of oil and gas engineering.With the widespread adoption of digital technologies in the oil and gas industry,data volumes have increased,yet connections among heterogeneous data remain insufficient.Conventional artificial intelligence methods have therefore shown limitations in data integration,knowledge uti-lization,and adaptation to complex operating conditions.The development of a new generation of artificial intelligence technologies represented by large language models has provided new technical support for multimodal perception,semantic understanding,causal reasoning,and decision generation,while opening new pathways for the intelligent evolution of integrity management.Against this background,this paper focuses on large-model-driven transformation in integrity management.It examines the capability boundaries of conventional artificial intelligence methods in multimodal data integration,knowledge utilization,and few-shot generalization,and systematically reviews an intelligent management architecture driven by large models.A five-layer pyramid framework is used to organize the integrity management process,establishing corresponding relationships among data fusion and perception,intelligent identification and diagnosis,risk assessment and prediction,adaptive evaluation,and intelligent decision-making.The framework further clarifies information transfer across layers by linking data input with condition identification and transmitting risk analysis results to the evaluation and decision-making stages,thereby forming an information flow for integrity management.The paper also analyzes progress in cross-modal integration,causal modeling,and emergent capabilities,and discusses the influence of large models on integrity-management modeling from the perspectives of knowledge representation and reasoning and few-shot transfer.Based on representative cases from China and other countries,it summarizes the use of large models in safety monitoring,accident tracing,and report generation.It further reviews the application of retrieval-augmented generation,causal reasoning,and intelligent agents in scenarios such as compliance review and leakage monitoring,discusses the coordination of large models with domain knowledge and other model components,and analyzes the functional roles of different technical modules in data processing,condition analysis,and decision support,together with their integration into existing technical systems.Finally,the paper emphasizes human-machine collaboration and discusses development pathways in terms of feasibility,interpretability,and sustainability.Focusing on engineering issues including model hallucination,deployment resources,data security,multimodal adaptation,and standardization,it examines the constraints on model validation and decision execution when large models are introduced into oil and gas applications.The study provides a theoretical reference and practical basis for developing more reliable and efficient integrity management systems in the oil and gas industry.关键词
油气生产系统/完整性管理/多模态大模型/智能决策Key words
oil and gas production system/integrity management/multimodal large model/intelligent decision-making分类
能源科技引用本文复制引用
曹倩雯,聂一凡,王金江,张来斌..基于大模型的油气生产系统完整性管理:现状与挑战[J].石油科学通报,2026,11(4):1096-1109,14.基金项目
油气重大专项"深远海油气生产安全保障技术及海上应急抢修技术"课题"深远海水下生产设施智能监检测及安全保障技术研究"(2025ZD1403701)、国家自然科学基金项目资助(62402526)和中国石油大学(北京)科研基金(2462024BJRC013)联合资助 （2025ZD1403701）
