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

CDS

• 批准号: 2203821
• 负责人: Edmond Chow
• 金额: $ 65.09万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203821&HistoricalAwards=false
• 关键词: CDS& Sustained; risk; mitigation; carbon

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.

气候小组一致认为，碳捕获和储存是减少大气中温室气体和应对气候变化的任何综合计划的重要组成部分。二氧化碳捕获和储存的更广泛接受受到了二氧化碳从用于储存的地下水库泄漏的风险的阻碍。 为了帮助管理风险，任何CCS奋进都必须使用监测系统来检测泄漏，例如地震监测。该项目解决了在不确定的未来可持续发展的地震监测系统的发展中的几个挑战。这些挑战包括来自永久性和低成本传感器的噪声数据，描述地下二氧化碳羽流所需的高维数据，对不确定性量化的需求以确保利益相关者，以及连续监测系统的泄漏自动检测。该项目包括扩大参与计算科学的活动，包括开发和部署面向不同背景，兴趣和职业目标的学生的科学应用机器学习短期课程。该项目的技术目标是开发一种监测地下储存库中关键二氧化碳羽流的方法，该方法结合了时间推移地震成像，物理模拟，不确定性量化。 这项工作扩展了目前的做法，使用贝叶斯数据同化与流体流动模拟的二氧化碳羽流。为了便于处理，使用条件可逆神经网络来表示复杂的概率分布。数据同化还与联合恢复方法相结合。二氧化碳羽流的后验分布被输入到生成分类器中，以自动检测泄漏，同时估计不确定性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learned multiphysics inversion with differentiable programming and machine learning

• DOI: 10.1190/tle42070474.1
• 发表时间: 2023-04
• 期刊: ArXiv
• 作者: M. Louboutin;Ziyi Yin;Rafael Orozco;Thomas J. Grady;Ali Siahkoohi;G. Rizzuti;Philipp A. Witte;O. Møyner;G. Gorman;F. Herrmann

Model-parallel Fourier neural operators as learned surrogates for large-scale parametric PDEs

• DOI: 10.1016/j.cageo.2023.105402
• 发表时间: 2022-04
• 期刊: Comput. Geosci.
• 作者: Thomas J. Grady;R. Khan;M. Louboutin;Ziyi Yin;Philipp A. Witte;Ranveer Chandra;Russell J. Hewett;F. Herrmann

Wave‐based inversion at scale on graphical processing units with randomized trace estimation

通过随机轨迹估计，在图形处理单元上进行基于 Wave 的大规模反演

• DOI: 10.1111/1365-2478.13405
• 发表时间: 2023
• 期刊: Geophysical Prospecting
• 影响因子: 2.6
• 作者: Louboutin, Mathias;Herrmann, Felix J.
• 通讯作者: Herrmann, Felix J.

Optimized time-lapse acquisition design via spectral gap ratio minimization

通过光谱间隙比最小化优化延时采集设计

• DOI: 10.1190/geo2023-0024.1
• 发表时间: 2023
• 期刊: GEOPHYSICS
• 影响因子: 3.3
• 作者: Zhang, Yijun;Yin, Ziyi;López, Oscar;Siahkoohi, Ali;Louboutin, Mathias;Kumar, Rajiv;Herrmann, Felix J.
• 通讯作者: Herrmann, Felix J.

Derisking geologic carbon storage from high-resolution time-lapse seismic to explainable leakage detection

消除从高分辨率延时地震到可解释泄漏检测的地质碳储存风险

• DOI: 10.1190/tle42010069.1
• 发表时间: 2023
• 期刊: The Leading Edge
• 作者: Yin, Ziyi;Erdinc, Huseyin Tuna;Gahlot, Abhinav Prakash;Louboutin, Mathias;Herrmann, Felix J.
• 通讯作者: Herrmann, Felix J.