An Edge-Enabled Cyber-Physical Framework for Real-Time Reservoir Surveillance and Optimization Using Autonomous Sensor Networks and AI-Based Analytics
DOI:
https://doi.org/10.64137/XXXXXXXX/IJCSEI-V1I1P102Keywords:
Edge computing, Cyber-physical systems, Reservoir surveillance, AI-based analytics, Autonomous sensor Networks, Real-time optimization, Machine learningAbstract
Effective reservoir monitoring and improvement are vital for achieving more recovery of hydrocarbons and saving money. Typical monitoring methods regularly have a small temporal and spatial resolution, late processing of new data, and problematic decision making. A new cyber-physical architecture is presented in this paper with the help of autonomous sensors and AI analytics for live monitoring and controlling reservoirs. Edge computing nodes connected to intelligent sensors through the proposed system make it possible to process data locally, reduce the time it takes to respond, and react more effectively in changing underground environments. The architecture is split into several levels to easily connect physical sensors, the layer processing information on edge devices, and the main cloud resources. Advanced automated learning is used at the edge to discover anomalies, monitor pressure-flow patterns, and improve the operations of the equipment. In comparison with traditional data management systems, using field-inspired simulations makes systems react more quickly, provide better data, and support more reliable decisions. They demonstrate that linking cyber technology with AI provides new opportunities for making reservoir management more responsive and adaptable
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