Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO_2 Plumes via Deep Learning
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We introduce three algorithms that invert simulated gravity data to 3D subsurface rock/flow properties. The first algorithm is a data-driven, deep learning-based approach, the second mixes a deep learning approach with physical modeling into a single workflow, and the third considers the time dependence of surface gravity monitoring. The target application of these proposed algorithms is the prediction of subsurface CO_2 plumes as a complementary tool for monitoring CO_2 sequestration deployments. Each proposed algorithm outperforms traditional inversion methods and produces high-resolution, 3D subsurface reconstructions in near real-time. Our proposed methods achieve Dice scores of up to 0.8 for predicted plume geometry and near perfect data misfit in terms of μGals. These results indicate that combining 4D surface gravity monitoring with deep learning techniques represents a low-cost, rapid, and non-intrusive method for monitoring CO_2 storage sites.
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