School of Mechanical and Electrical Engineering, Chengdu University of Technology
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology

针对地质钻进过程中钻遇对象未知且复杂多变、钻进事故频发的现状，采用数字孪生技术，构建了基于时序数据驱动的地质钻探数字孪生模型体系，用以满足随钻探测、工况识别、钻速优化等实际需求。将监测到的地表设备、随钻测量、钻进工艺等数据按照时间序列分解为事前数据、实时数据、延时数据和迟到数据，利用物联网技术将这些多源异构数据进行处理，采用时序数据进行特征分析，基于事前数据建立典型工况，实时数据进行随钻预测和钻进过程工况识别，延时数据和迟到数据演进融合进行钻后优化，建立了数字孪生智能钻进周期服务平台，平台设计了设备物理层、虚拟模型层、数据处理层及钻探服务层4层交互系统，实现钻前、钻中、钻后全数据的全过程集成融合，达到了钻进系统参数最优化配置和安全高效钻探的目的。基于上述平台，利用Unity3D软件开发了数字孪生智能钻进原型系统，实现了钻前设备的数字化设计、钻进过程孔内三维可视化和钻进过程参数实时监测与控制的功能。结果表明，基于时序数据构建的数字孪生模型可有效提高钻进过程的效率和可靠性。研究结果为智能钻探优化提供了全新的路径和方法，有望在煤炭、石油、天然气、页岩气等钻探领域实现工程应用。

In view of the situation of unknown, complex and variable drilling objects and frequent drilling accidents during the geological drilling, a digital twin model system of geological drilling based on time series data was built with the digital twin technology, so as to meet the actual requirements of prediction while drilling, condition identification, drilling rate optimization and others. The monitored data of ground equipment, measurement-while-drilling and drilling process were decomposed into prior data, real-time data, delayed data and late data according to the time series. On this basis, these multi-source heterogeneous data were processed by the Internet of Things, characteristic analysis was carried out with the time series data, the typical operating conditions were established based on the prior data, the conditions of prediction while drilling and that during the drilling were identified based on real-time data, and the time series evolution was integrated with the delayed and late data for post-drilling optimization. Then, the digital twin based intelligent drilling full-cycle service platform was established, this platform has been designed with a four interactive systems of equipment physical layer, virtual model layer, data processing layer and drilling service layer, which realizes the full-process integration of the prior data, real-time data, and delayed data. Thus, the purposes of optimal configuration of the drilling system parameters and drilling with high safety and efficiency have been achieved. Based on the above platform, the digital twin based intelligent drilling prototype system was developed using the Unity3D software, which realized the functions of digital design of pre-drilling equipment, 3D visualization of the drilling process and real-time monitoring and controlling of drilling parameters. The results show that the digital twin model based on time series data could effectively improve the efficiency and reliability of the drilling process. Besides, the research results could provide a new path and method for intelligent drilling optimization under the complex geological conditions, which is expected to be applied in coal, oil, natural gas, shale gas drilling and other fields.

国家自然科学基金项目(42072344)；国家自然科学基金青年基金项目(41302243)；国家重点实验室自由探索课题(SKLGP2017Z007)

姜杰，霍宇翔，张颢曦，等. 基于数字孪生的智能钻探服务平台架构[J]. 煤田地质与勘探，2023，51(9)：129−137.

JIANG Jie，HUO Yuxiang，ZHANG Haoxi，et al. Architecture of intelligent service platform for drilling based on digital twin[J]. Coal Geology & Exploration，2023，51(9)：129−137.