基于石油科技实践中数据回流滞后、孪生验证不足和现场协同决策分散的情况，对人工智能与数字孪生融合驱动的创新模式进行了研究。阐述了油气全链数据、站场管网映射和智能算法应用基础，介绍了末端运行数据反向牵引、孪生沙盘策略验证和智能推理协同决策的实践路径。

In response to challenges in petroleum technology practice—including delays in data feedback, insufficient digital twin validation, and fragmented on-site collaborative decision-making—this study investigates an innovation model driven by the integration of artificial intelligence and digital twins. It outlines the foundational elements of full-chain oil and gas data, field and pipeline network mapping, and intelligent algorithm applications, and presents practical approaches for leveraging operational data from the field to drive decision-making, validating strategies through digital twin simulations, and enabling collaborative decision-making through intelligent reasoning.