摘要：

油气生产系统完整性管理是保障设备安全运行、环境合规与生产连续性的核心环节，贯穿油气工程全生命周期。随着油气行业数字化技术的普及，数据量增大，但数据之间关联不足，传统人工智能方法在数据整合、知识利用与复杂工况适应能力等方面暴露出明显局限。以大语言模型代表的新一代人工智能技术的发展，为多模态感知、语义理解、因果推理和决策生成提供了新的技术支撑，也为油气生产系统完整性管理的智能化演进提供了新的技术路径。在此背景下，本文聚焦以大模型为核心的智能化变革，剖析传统人工智能方法在多模态数据整合、知识利用及小样本泛化等方面的能力边界，系统梳理大模型驱动智能管理架构，并以5层金字塔体系组织完整性管理流程，使数据融合与感知、智能识别与诊断、风险评价与预测、自适应评估和智能决策形成对应关系。该架构明确了各层之间的信息传递关系，使数据输入与状态识别相连接，并将风险分析结果传递至评估和决策环节，形成面向完整性管理流程的信息传递链。进一步地，本文多维度分析大模型在跨模态整合、因果建模及涌现能力方面的进展，从知识表示与推理、少样本迁移等方面说明大模型对完整性管理建模方式的影响。结合国内外典型案例，阐述大模型在安全监测、事故溯源及报告生成中的智能化优势，归纳检索增强、因果推理和智能体等技术在合规审查、泄漏监测等场景中的使用方式，讨论大模型与领域知识和模型组件的协同方式，分析不同技术模块在数据处理、状态分析与决策支持环节中的功能关系及其与现有技术体系的衔接方式。最后强调人机协同的重要性，探讨可行性、可解释性与可持续性发展路径，围绕模型幻觉、部署资源、数据安全、多模态适配和标准化等工程问题，分析大模型进入油气场景后在模型验证和决策执行中的约束，为油气行业构建更可靠高效的完整性管理体系提供理论参考和实践思路。

关键词: 油气生产系统, 完整性管理, 多模态大模型, 智能决策

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 utilization, 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.

Key words: oil and gas production system, integrity management, multimodal large model, intelligent decision-making