Abstract: To reduce the complex drilling risks induced by uncertain geological conditions in deep formations, the digital twin technology has been adopted to construct a digital twin intelligent risk early warning system for complex drilling risks based on the fusion of physical models and data-driven models. In order to meet the actual requirements of early warning while drilling and risk reduction, four supporting technologies of digital twin early warning system were proposed, including microservice-based data integration, intelligent perception of digital twins, multimodal fusion early warning, and intelligent diagnosis of digital twins. The overall architecture of the digital twin early warning system for drilling has been established, and its functions and model design were described in detail. The system involved a five-layer interaction system, including physical device layer, virtual entity layer, digital twin data layer, digital twin algorithm model layer, and digital twin early warning service layer. Three application scenarios have been designed, including pre-drilling risk avoidance rehearsal, real-time warning during drilling, and post-drilling analysis of block risk situations to optimize block drilling design. This system has achieved several functions, such as digital integration of multi-source heterogeneous data, multiple fusion of traditional physical models with intelligent models, warning and type identification of overflow, lost circulation, and pipe sticking, etc. Therefore, the risks associated with deep drilling operations have been reduced and drilling efficiency has been optimized. The study results indicate that the digital twin warning architecture based on “model + data” has the potential to identify drilling risks and diagnose risk types in advance during drilling, providing new technology approaches for intelligent drilling risk early warning.