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

美国80%的能源需求由地下资源满足，而深层地质构造也被用作废物储存库，例如在拟议的碳储存行动中。然而，深层的活动有可能影响上覆浅层含水层的饮用水，这些含水层容易受到泄漏的盐水、水力压裂液或气体的污染。因此，为了管理深层水库的开采和储存作业，并尽量减少对它们的环境影响，既需要了解造成潜在泄漏的过程，也需要了解监测这种泄漏的方法。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广泛收集数据，这对深层地层来说是非常昂贵的。这项研究的目的是开发一种创新的方法，将浅层含水层的所有可用数据和来自深层地质区的通常有限的数据结合起来，以改进对不利环境影响的监测。这种新方法在实验室中得到验证后，将使用更容易从浅层含水层获得的数据，从而减少对深部地层进行昂贵钻探的需要。将开发的这项新科学将允许从深层更安全地提取能源，并提供更安全的二氧化碳地下储存，以缓解可能对人类、生态和环境造成影响的全球气候变化。与在两所大学执行这项研究相关的培训机会将有助于科学技术人员能力建设和新劳动力的发展，这将解决水-能源联系中出现的新问题。主要目标是开发并实验验证一种新的逆理论，该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，以改进深层地质区的特征以及监测相连的上覆含水层的潜在污染。对于数据贫乏的地下系统，现有的假设边界条件(BC)的技术可能导致不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，提出的理论没有使用正向模拟来评估模型数据的失配。因此，不需要难以确定的站点BC的知识。相反，它强制流体流动和/或溶质质量连续，以有限和噪声测量为条件。在这项研究中，这一理论将通过(1)用于深部地层水力表征的反压力和流动观测，以及(2)用于深部区域表征和渗漏监测的联合反演流和水质数据来进一步发展和检验。这一新理论能够同时进行参数估计和BC估计，并已成功地用合成数值数据进行了检验。然而，为该领域的理论验证生成准确和全面的数据是不可行的。因此，提出了一种使用中等规模实验室试验台数据的方法。该实验方法允许在实验室中创建不同的含水层非均质性，并精确控制模拟深部作业的流动和运移初始和BC。这一理论首先将通过将实验室含水层中的测量结果估计的参数与准确的已知参数和BC进行比较来检验。作为第二步，将在由漏水含水层隔开的双层含水层中进行水力和示踪测量。来自浅层无约束层和深层受限层(即扰动源)的数据将被联合反演，以描述整个系统的特征，并确定来自深层的泄漏路径和速率。反演算法将通过相同试验床(即固定填料)的独立测量来验证，但在不同的BC和泄漏情况下。在这一验证之后，将使用从代表具有地质相关参数和条件的深部地层的模型中获得的合成数据来证明该理论。新方法旨在使数据匮乏环境的表征和监控更加准确和高效，使这项研究在理论发展和实际问题解决方面都具有潜在的变革性。

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.