Yue, Q.; Liu, F.; Diao, Y., and Liu, Y., 2018. Research and application of a big data-driven intelligent reservoir management system. In: Ashraf, M.A. and Chowdhury, A.J.K. (eds.), Coastal Ecosystem Responses to Human and Climatic Changes throughout Asia.

In view of multisource information integration difficulties, the lack of real-time data acquisition capability, and the low level of management intelligence in current reservoir management, the intelligent monitoring node of a remote internet of things is applied for real-time acquisition of reservoir operation data and the establishment of a four-layer browser and server system framework. An intelligent reservoir management system with flexible configuration and strong scalability is developed based on the dynamic big data drive concept. The system achieves highly efficient application of the internet of things and cloud computing technology in the reservoir management field; fully considers the high demand of the massive monitoring data on the management system; integrates reservoir health and the operation of big data analysis; can conduct accurate monitoring, diagnosis, analysis, forecasting, and management optimization of the reservoir operation condition' and achieves real-time output through charts. The system's user can get a quick access to multisource data sharing and decision support services via LED display and computer and intelligent mobile terminals. The demonstration project application shows that the system has integrated application of massive data and practical, scalable, and user-friendly features, which can provide comprehensive and efficient information technology support for the intelligent management of reservoir operation and has broad application prospects.