Abstract:

This paper systematically reviews the current technical framework and implementation approaches for drilling digital twin modeling, focusing on key challenges such as multi-source heterogeneous data fusion during wellbore drilling operations, the coupling of physics-based and data-driven models, and intelligent decision feedback mechanisms. First, targeting the objects and levels of drilling data fusion, the fusion mechanisms for multi-temporal and multi-spatial scale data and conflict resolution methods were explored. Second, a comprehensive digital twin architecture suitable for drilling operating conditions was established, and methods for implementing joint physics-based and data-driven modeling were summarized. Then, diagnostic methods for drilling anomaly data features were proposed, enabling the establishment of multi-perspective decision feedback mechanisms between the physical and digital entities. Finally, the application potential of wellbore digital twin technology was prospected in the areas of high spatiotemporal resolution cognition, edge deployment, and model credibility assurance. The research results can provide theoretical support and methodological guidance for achieving drilling state recognition and efficient control under complex operating conditions.

Key words: oil and gas, wellbore digital twin, multi-source data fusion, intelligent decision-making, data-physics driven

摘要：

本文围绕井眼钻井过程中的多源异构数据融合、物理与数据驱动模型耦合以及智能决策反馈等关键问题，系统梳理了当前钻井数字孪生建模的技术框架与实现路径。首先，针对钻井数据融合对象与层次，探讨了多时空尺度数据的融合机制和冲突处理方法。其次，构建了一个适用于钻井工况的数字孪生总体架构，总结了实现机理与数据的联合驱动建模的方法。然后，提出了钻井异常数据特征诊断方法，并以此建立实体与数字体间的多视角决策反馈方法。最后，展望了井眼数字孪生技术在高时空分辨率认知、边端部署、模型可信性保障等方面的应用潜力。研究结果可为实现复杂工况下的钻井状态识别与高效控制提供理论支撑与方法指导。

关键词: 油气, 井眼数字孪生, 多源数据融合, 智能决策, 数据－机理驱动

CLC Number: 

• TE21

• TP183