A practical exploration of intelligent operation and maintenance system for deep drilling equipment

Abstract:

The operating environment for deep drilling is extreme， where critical components are prone to issues such as wear， fatigue， and seal failure. Traditional operation and maintenance （O&M） models， primarily based on scheduled maintenance and reactive repair， struggle to meet the requirements for continuous operation and operational safety. Addressing the intelligent O&M requirements for deep drilling equipment， this paper systematically analyzes key requirements including user roles， system deployment， data acquisition and transmission， application scenarios， and functional configuration. It proposes a technical scheme for multi-source heterogeneous data integration and transmission， establishing an integrated approach that coordinates WITSML/ETP， OPC UA， and Kafka. A distributed and elastic "cloud-edge-device" collaborative architecture is constructed， introducing K3s and KubeEdge for edge service deployment and cloud-edge collaboration， while employing time-series databases and graph databases for data and knowledge management. Based on this， a prototype software for the intelligent O&M system is developed， integrating functions such as digital twin， real-time monitoring， and early fault warning. The applicability of the system under engineering conditions is validated using steel wire rope electromagnetic inspection as a case study. This research provides an implementable technical architecture and pathway for intelligent O&M of deep drilling equipment， offering engineering reference value for enhancing equipment operational reliability and O&M efficiency under extreme conditions.