A modeling paradigm for unconventional reservoir parameters based on an interpretable neural network
In the past few decades, the machine learning (or data-driven) approach has been broadly adopted as an alternative to scientific discovery, resulting in many opportunities and challenges. In the oil and gas sector, subsurface reservoirs are heterogeneous porous media involving a large number of complex phenomena, making their characterization and dynamic prediction a real challenge. This study provides a comprehensive overview of recent research that has employed machine learning in three key areas: reservoir characterization, production forecasting, and well test interpretation. The result
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