Abstract: 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.