Collaborative Research: A New Inverse Theory for Joint Parameter and Boundary Conditions Estimation to Improve Characterization of Deep Geologic Formations and Leakage Monitoring

合作研究：联合参数和边界条件估计的新逆理论，以改善深层地质构造和泄漏监测的表征

• 批准号: 1702078
• 负责人: Ye Zhang
• 金额: $ 24.25万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702078&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Inverse; Theory

Eighty percent of U.S. energy demands are met by subsurface resources while deep geologic formations are also used as waste repository such as in the proposed actions of carbon storage. However, activities in deep zones have the potential to impact potable water in overlying shallow aquifers that is subject to contamination from leaking brine, hydraulic fracturing fluids, or gasses. Hence, to manage extraction and storage operations from deep reservoirs and to minimize the environmental impact on them, both an understanding of the processes that contribute to potential leakage and the methods to monitor such leakage are needed. This information helps to evaluate environmental risks and to assess corrective actions. The methods that are currently available for such understanding require extensive data collection from the subsurface which is very costly for deep formations. This research aims to develop an innovative method to integrate all available data from both shallow aquifers and often limited data from deep geologic zones to improve the monitoring of adverse environmental impacts. This new method, when validated in the laboratory, will use more easily available data from the shallow aquifers, thus reducing the need for costly drilling into deep formations. The new science that will be developed will allow for safer extraction of energy from deep formations and provide more secure subsurface storage of carbon dioxide, a greenhouse gas, in order to mitigate global climate change that has potential human, ecological, and environmental impacts. The training opportunities associated with the execution of this research at two universities will contribute to scientific and technical human capacity building and new workforce development that will address emerging problems at the water-energy nexus.The primary goal is to develop, and experimentally verify, a novel inverse theory that integrates limited data from deep formations with more abundant or easily obtainable shallow aquifer data for improved characterization of deep geologic zones as well as for the monitoring of connected, overlying aquifers for potential contamination. For data-poor subsurface systems, existing techniques that assume boundary conditions (BC) can result in non-unique and uncertain parameter estimates, leading to inaccurate models. Compared to the earlier techniques, the proposed theory does not use forward simulations to assess model-data misfits. Thus the knowledge of the difficult-to-determine site BC is not required. Instead, it imposes fluid flow and/or solute mass continuities conditioned to limited and noisy measurements. In this research, the theory will be further developed and tested by (1) inverting pressure and flow observations for hydraulic characterization of a deep formation, and (2) jointly inverting flow and water quality data for both deep zone characterization and leakage monitoring. This new theory, which is capable of simultaneous parameter and BC estimation, has been successfully tested with synthetic numerical data. Generation of accurate and comprehensive data for theory validation in the field is however not feasible. Thus, an approach that uses data from intermediate-scale laboratory testbeds is proposed. The experimental method allows for the creation of different aquifer heterogeneities in the laboratory and the accurate control of flow and transport initial and BC that emulate deep zone operations. The theory will be first tested by comparing parameters estimated using measurements made in a laboratory aquifer with accurately known parameters and BC. As a second step, hydraulic and tracer measurements will be made in a two-layered aquifer separated by a leaky aquitard. Data from both the shallow unconfined layer and the deep confined layer (i.e., source of the disturbance) will be jointly inverted to characterize the entire system and to identify leakage pathways and rates from the deep layer. The inversion algorithms will be validated by independent measurements from the same testbeds (i.e., fixed packing), but under different BC and leakage scenarios. After this validation, the theory will be demonstrated using synthetic data taken from a model representing a deep formation with geologically relevant parameters and conditions. The new method will aim to make characterization and monitoring more accurate and efficient for data-poor environments, making this research potentially transformational in both theory development and practical problem solution.

美国80%的能源需求是由地下资源满足的，而深层地质构造也被用作废物储存库，例如在碳储存的拟议行动中。然而，深层的活动有可能影响上覆浅层含水层中的饮用水，这些含水层受到泄漏盐水、水力压裂液或气体的污染。因此，为了管理深层储层的开采和储存作业，并尽量减少对这些作业的环境影响，既需要了解造成潜在渗漏的过程，也需要了解监测这种渗漏的方法。这些信息有助于评估环境风险和评估纠正措施。目前可用于这种理解的方法需要从地下收集大量数据，这对于深部地层来说是非常昂贵的。这项研究的目的是开发一种创新方法，将浅层含水层的所有可用数据和深层地质区的有限数据结合起来，以改善对不利环境影响的监测。这种新方法在实验室得到验证后，将使用更容易获得的浅层含水层数据，从而减少对深层地层昂贵钻探的需求。将开发的新科学将允许更安全地从深层地层中提取能量，并提供更安全的二氧化碳地下储存，以减轻对人类，生态和环境具有潜在影响的全球气候变化。在两所大学开展这项研究的相关培训机会将有助于科学和技术人员能力建设和新的劳动力开发，以解决水-能源关系中出现的问题。一种新的反演理论，将来自深层地层的有限数据与更丰富或更容易获得的浅层含水层数据相结合，此外，还可利用这一技术来确定深层地质区的特征，并监测相连的上覆含水层是否存在潜在的污染。对于数据贫乏的地下系统，假设边界条件（BC）的现有技术可能导致非唯一和不确定的参数估计，从而导致不准确的模型。与早期的技术相比，所提出的理论不使用正向模拟来评估模型数据失配。因此，不需要了解难以确定的部位BC。相反，它强加流体流量和/或溶质质量连续性条件有限和嘈杂的测量。在这项研究中，该理论将进一步发展和测试（1）反演压力和流量观测的水力表征的深层地层，（2）联合反演流量和水质数据的深层区域表征和泄漏监测。这个新的理论，这是能够同时参数和BC估计，已成功地测试与合成数值数据。然而，在实地生成用于理论验证的准确和全面的数据是不可行的。因此，提出了一种使用来自中等规模实验室测试台的数据的方法。实验方法允许在实验室中创建不同的含水层非均质性，并精确控制模拟深层操作的流量和运输初始和BC。该理论将首先通过比较使用在实验室含水层中进行的测量所估计的参数与准确已知的参数和BC进行测试。作为第二步，将在由渗漏弱透水层隔开的两层含水层中进行水力和示踪测量。来自浅无约束层和深约束层的数据（即，扰动源）的数据进行联合反演，以确定整个系统的特征，并确定深层的渗漏路径和渗漏率。反演算法将通过来自相同测试台的独立测量进行验证（即，固定包装），但在不同的BC和泄漏情景下。验证后，将使用从代表深层地层的模型中获取的合成数据（具有地质相关参数和条件）来证明该理论。新方法的目标是使表征和监测更准确和有效的数据贫乏的环境，使这项研究在理论发展和实际问题的解决方案潜在的变革。

项目成果

A new inverse method for contaminant source identification under unknown solute transport boundary conditions

未知溶质运移边界条件下污染源识别的新逆方法

• DOI: 10.1016/j.jhydrol.2019.123911
• 发表时间: 2019
• 期刊: Journal of Hydrology
• 影响因子: 6.4
• 作者: Jiao, Jianying;Zhang, Ye;Wang, Liqiang
• 通讯作者: Wang, Liqiang

Exploring the Impacts of Source Condition Uncertainties on Far‐Field Brine Leakage Plume Predictions in Geologic Storage of CO2: Integrating Intermediate‐Scale Laboratory Testing With Numerical Modeling

• DOI: 10.1029/2021wr029679
• 发表时间: 2021-08
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: A. H. Askar;T. Illangasekare;A. Trautz;J. Solovský;Ye Zhang;R. Fučík