A sensor selection optimization framework for tracking CO2 flow movements in carbonates

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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 domains in subsurface sensing, in particular. In this work, 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 uncertain estimation of sensor data, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the reservoir formation. The results are promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.