Abstract: As the demand for deep and ultra-deep energy resource exploration escalates, the intelligent operation and maintenance of drilling equipment in complex conditions is challenging due to the need for efficiency and safety. Traditional mechanism-based or data-driven approaches are inadequate for dynamic adaptation and closed-loop optimization, indicating the necessity of virtual-physical fusion technology for intelligent operation and maintenance. This paper expounds on the multifaceted challenges of operation and maintenance for drilling equipment in deep and ultra-deep environments, systematically summarizing the necessity of virtual-physical fusion technology in enhancing multi-source data mining potential, mechanism-data integration capabilities, and the intelligence level of decision-making. The paper puts forward a digital twin-driven virtual-physical fusion intelligent operation and maintenance framework for drilling equipment, which comprehensively improves operation and maintenance levels through pre-drilling risk assessment, real-time intelligent control during drilling, and post-drilling data optimization services. Theoretical and technical support is provided for the full lifecycle management of drilling equipment. Finally, based on the virtual-physical fusion intelligent operation and maintenance technology system, practical applications were carried out in the performance evaluation of blowout preventers, the improvement of system intelligent monitoring and detection, and the optimization of drilling pump data mining, providing ideas for the application of virtual real fusion intelligent operation and maintenance technology in the oil and gas field.