石油工业技术监督

面对资源品质下降、勘探目标日趋复杂、成本持续攀升及环保约束日益收紧的多重挑战，人工智能已成为驱动油气行业变革的关键力量，而其效能发挥从根本上依赖于高质量、可治理的数据基础。在此背景下，数据治理的作用已从传统的数据管理环节，跃升为赋能全业务链智能化转型的战略基石。系统阐述了数据治理在油气勘探开发数智化转型中的核心赋能作用。研究指出，通过构建面向人工智能的智能化数据治理体系，能够整合覆盖“油气藏—井筒—地面”的全链条多源数据，形成统一、可信的数据资产。在此基础上，通过建立“大模型+小模型”协同机制，推动油气业务向数据驱动、实时优化、闭环智能的高级阶段演进，从而为构建覆盖全价值链的人工智能应用体系提供理论支撑与实践路径。

Faced with the multiple challenges of declining resource quality, increasingly complex exploration objectives, rising costs, and tightening environmental constraints, artificial intelligence has become a key force driving the change in the oil and gas industry, and its effectiveness fundamentally depends on high-quality and manageable data foundation. In this context, the role of data governance has jumped from the traditional data management link to the strategic cornerstone of enabling the intelligent transformation of the whole business chain. This paper systematically expounded the core enabling role of data governance in the digital and intelligent transformation of oil and gas exploration and development. The research has shown that by constructing an intelligent data governance system for artificial intelligence, it is possible to integrate multi-source data covering the entire chain from "oil and gas reservoirs" to "wellbores" and "surface facilities", and form a unified and trustworthy data asset. On this basis, the collaboration mechanism of "big model + small model" has been established to promote the evolution of oil and gas business to the advanced stage of data-driven, real-time optimization and closed-loop intelligence, thereby providing theoretical support and practical path for building an artificial intelligence application system covering the whole value chain.