Abstract
4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas, in particular in subsurface sensing. We present a novel 4IR inspired framework for the real-time sensor selection for subsurface pressure and temperature monitoring, as well as reservoir evaluation. The framework encompasses a deep learning technique for sensor data uncertainty estimation, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the CO2 penetration in the reservoir. The results are rather promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.
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Katterbauer, K., Al Shehri, A., Al Qasim, A. (2022). Real-Time Intelligent Sensor Selection for Subsurface CO2 Flow and Fracture Monitoring. In: Silhavy, R. (eds) Artificial Intelligence Trends in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-09076-9_23
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DOI: https://doi.org/10.1007/978-3-031-09076-9_23
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