CDS&E: Collaborative Research: A Bayesian inference/prediction/control framework for optimal management of CO2 sequestration

CDS

• 批准号: 1507009
• 负责人: Georg Stadler
• 金额: $ 14万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1507009&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Bayesian; inference

1508713 (Ghattas) / 1507488 (Willcox)/ 1507009 (Stadler)The focus of the proposed work is on integrating research developments in scientific computing, statistical analysis, and numerical analysis to provide a common platform for managing CO2 storage. Results from this work will be important to energy production in the US, an area of National interest. Geological carbon storage faces two main challenges: the risk of inducing seismicity, and leakage of the injected CO2 into potable aquifers. The characterization of the injection site and continued monitoring of the CO2 migration as well as stress changes in the region of elevated pressure are therefore particularly important to maximize the amount of CO2 that can be stored, while ensuring the long term safety of storage sites. To address these challenges, the overall goal of the proposed research is to (1) integrate well pressure and, where available, surface deformation data into coupled poromechanics models by solving the inverse problem for unknown subsurface properties; (2) to quantify the uncertainty in the inversion for the subsurface properties, and (3) to use the resulting inferred poromechanics models together with their uncertainty to design optimal control strategies for well injection that optimize the amount of stored CO2 while controlling the risk of seismicity. It is essential that this poromechanics based inference/prediction/control framework takes into account uncertainties at every stage, since both the observational data and the models are uncertain. However, solving stochastic inverse/optimal control problems for large-scale PDE models, such as those of poromechanics, is intractable using current methods, which suffer from the "curse of dimensionality." Thus, it is proposed to overcome these barriers by developing scalable methods and algorithms that exploit the problem structure to reduce effective dimensionality. While the end application of CO2 storage is quite important in itself, the framework to be developed can be applicable to a broader set of science and engineering problems for which large-scale uncertain models must be inferred from large-scale uncertain data, and then used to solve optimal decision-making problems under uncertainty.

1508713（GHATTAS） / 1507488（WILLCOX） / 1507009（Stadler）拟议工作的重点是将研究发展整合到科学计算，统计分析和数值分析中，以提供用于管理CO2存储的通用平台。 这项工作的结果对美国的能源生产至关重要。地质碳存储面临两个主要挑战：诱导地震性的风险以及将二氧化碳泄漏到饮用的含水层中。因此，注射部位的表征和持续监测二氧化碳迁移以及压力升高区域的压力变化对于最大化可以存储的二氧化碳量特别重要，同时确保储存位点的长期安全性。为了应对这些挑战，拟议的研究的总体目标是（1）通过解决未知地下特性的反问题，将井压力和表面变形数据整合到耦合的门力力学模型中； （2）为了量化地下属性的反转的不确定性，（3）使用所得的推断的门力学模型及其不确定性来设计最佳的控制策略，以优化储存的CO2的量，同时控制地震性风险。由于观察数据和模型都不确定，因此至关重要的是，基于Poromegaronics的推理/预测/控制框架在每个阶段都必须考虑到不确定性。但是，解决大规模PDE模型的随机逆/最佳控制问题（例如Poromechanics的模型）是使用当前方法棘手的，这些方法遭受了“维度的诅咒”。因此，建议通过开发可扩展的方法和算法来利用问题结构以降低有效维度来克服这些障碍。尽管二氧化碳存储的结束本身很重要，但要开发的框架可以适用于一组更广泛的科学和工程问题，必须从大规模的不确定数据中推断出大规模不确定模型，然后用来解决不确定的最佳决策问题。

项目成果

A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem

非线性反问题的 A 最优编码权重，及其在亥姆霍兹反问题中的应用

• DOI: 10.1088/1361-6420/aa6d8e
• 发表时间: 2017
• 期刊: Inverse Problems
• 影响因子: 2.1
• 作者: Crestel, Benjamin;Alexanderian, Alen;Stadler, Georg;Ghattas, Omar
• 通讯作者: Ghattas, Omar

Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations

• DOI: 10.1137/16m106306x
• 发表时间: 2017-01-01
• 期刊: SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION
• 影响因子: 2
• 作者: Alexanderian, Alen;Petra, Noemi;Ghattas, Omar
• 通讯作者: Ghattas, Omar