| 英文摘要: | 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.
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