CDS&E: Sustained risk mitigation of carbon storage with seismic monitoring through simulation and Bayesian inference

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Carbon capture and storage (CCS) is agreed upon by climate panels to be an essential part of any comprehensive plan for reducing greenhouse gases in the atmosphere and combating climate change. Broader acceptance of CCS is hindered by the perceived risk of carbon dioxide leakage from the underground reservoir being used for storage. To help manage risk, any CCS endeavor must use a system of monitoring to detect leaks, such as seismic monitoring. This project addresses several challenges in the development of a seismic monitoring system that is sustainable into the indeterminate future. These challenges include noisy data from permanent and low-cost sensors, high-dimensional data needed to describe the carbon dioxide plume underground, the need for uncertainty quantification to assure stakeholders, and automated detection of leaks for continuous monitoring systems. This project includes activities to broaden participation in computational science, including the development and deployment of a short course on machine learning for scientific applications geared to students with diverse backgrounds, interests, and career goals.The technical aim of this project is to develop a methodology for monitoring critical carbon dioxide plumes in underground storage reservoirs that combines time-lapse seismic imaging, physical simulation, and uncertainty quantification. The work extends current practice by using Bayesian data assimilation with a fluid flow simulation for the carbon dioxide plume. For this to be tractable, conditional invertible neural networks are used to represent complex probability distributions. The data assimilation is also coupled with joint recovery methods. The posterior distribution of the carbon dioxide plume is input into a generative classifier to automatically detect leakages while also estimating uncertainty.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.