Abstract: The growing demand for deep and ultra-deep drilling operations presents dual challenges in terms of efficiency and safety for the intelligent operation and maintenance of drilling equipment under complex working conditions. Conventional methodologies, whether relying solely on physical mechanisms or purely driven by data analytics, often fall short in delivering the dynamic adaptability and closed-loop optimization required in modern drilling contexts. Consequently, the integration of virtual and physical realms has emerged as an essential and enabling pathway toward achieving truly intelligent operation and maintenance. This study begins by systematically analyzing the multifaceted challenges encountered in the operation and maintenance of drilling equipment in deep and ultra-deep environments. These challenges span five critical and interconnected levels: at the equipment level, unclear failure propagation mechanisms in highly coupled systems; at the environmental level, extreme pressures, temperatures, and restricted sensing conditions; at the data level, heterogeneity, noise, and low utilization of multisource information; at the model level, the paradigm divide between physics-based and data-driven approaches; at the decision-making level, reliance on static rules and delayed human intervention. This analysis underscores the necessity of virtual–physical fusion technology that significantly enhances the potential for mining multi-source data, deepens the integration of mechanistic principles with data-driven insights, and elevates the overall intelligence of operational decisions. A digital twin-driven framework for virtual–physical fusion in intelligent operation and maintenance is proposed for drilling equipment. The framework is designed around four cohesive layers: intelligent perception, twin model, virtual–physical mapping, and operation and maintenance service. The intelligent perception layer employs advanced multidimensional data acquisition technologies, including physical sensors and virtual sensing techniques, to provide a real-time, comprehensive stream of status information from the formation, downhole tools, and surface equipment, thereby forming a reliable and holistic data foundation for the entire system. As the core of the framework, the twin model layer constructs high-fidelity multidomain digital twins that integrate mechanical, electrical, hydraulic, and control subsystems. These models achieve an accurate mapping of the state, behavior, and dynamic responses of the physical equipment, enabling precise simulation, fault propagation analysis, and forward-looking predictive assessment under diverse operational scenarios. The virtual–physical mapping layer ensures dynamic consistency and supports closed-loop optimization by maintaining a continuous bidirectional interaction and real-time state synchronization between the physical entity and its virtual counterpart. Furthermore, this layer enables model calibration and iterative refinement based on the streaming data, guaranteeing that the digital twin remains a faithful representation throughout the equipment lifecycle. Finally, the operation and maintenance service layer aims to reduce the drilling risk, enhance equipment reliability, and improve operational safety. The practical value of this virtual-physical fusion operation and maintenance framework is demonstrated through its application in several key areas: performance evaluation of blowout preventers under diverse shear conditions, intelligent monitoring and early fault detection for hoisting systems using digital twin-driven anomaly diagnosis, and data-driven optimization for drilling pumps through virtual–real data fusion and feature selection. These implementations validate the effectiveness of the framework in enhancing operational awareness, reducing unplanned downtime, and optimizing maintenance resources. Furthermore, they provide a valuable reference for the broader adoption and continued development of intelligent virtual–physical fusion solutions in the oil and gas industry.