Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO$_2$ Plumes via Deep Learning

• URL: http://arxiv.org/abs/2209.02850v1
• Date: Tue, 6 Sep 2022 23:24:20 GMT
• Title: Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO$_2$ Plumes via Deep Learning
• Authors: Adrian Celaya, Bertrand Denel, Yen Sun, Mauricio Araya-Polo, Antony Price
• Abstract summary: We introduce three algorithms that invert simulated gravity data to 3D subsurface rock/flow properties. Each proposed algorithm outperforms traditional inversion methods and produces high-resolution, 3D subsurface reconstructions in near real-time. Results indicate that combining 4D surface gravity monitoring with deep learning techniques is a low-cost, rapid, and non-intrusive method for monitoring CO$$ storage sites.
• Score: 24.70079638524539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\mu$Gals. 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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