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Abstract Monitoring brine leakage from CO2geological storages (CGS) is necessary to protect shallow aquifers against contamination. A framework for designing CGS monitoring systems that optimally use both easily available shallow zone data and hard‐to‐obtain deep zone observations is developed and validated. This framework is based on calibrating a transport model using monitoring data to determine leakage source conditions and then predict the subsequent brine plume that potentially contaminates shallow aquifers. As cost considerations are expected to limit monitoring deep formations, the framework is developed to minimize the number of deep observation points (e.g., deep sensors). The best monitoring locations that yield the most worthful data for reducing predictive uncertainty is selected by integrating linear uncertainty analysis with Genetic Algorithm under this framework. Due to practical challenges, testing such a framework in the field is not feasible. Thus, the framework was tested in an intermediate‐scale soil tank, where monitoring data on brine leakage plume development from the storage zone to the shallow aquifer were collected. Predictions made by a transport model calibrated on these data were then compared with experimental measurements to evaluate data informativity and thus validate the framework's applicability. The results demonstrate the framework ability to select the optimum monitoring locations for leakage detection and model calibration. It was also found that not only deep observations, but also shallow zone data are worthful to determine source conditions. Moreover, the results showed the possibility of identifying the likely areas to be impacted in the shallow aquifer using early stage monitoring data.
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Exploring the Impacts of Source Condition Uncertainties on Far‐Field Brine Leakage Plume Predictions in Geologic Storage of CO 2 : Integrating Intermediate‐Scale Laboratory Testing With Numerical Modeling
Abstract Natural fissures/faults or pressure‐induced fractures in the caprock confining injected CO2have been identified as a potential leakage pathways of far‐field native brine contaminating underground sources of drinking water. Developing models to simulate brine propagation through the overlaying formations and aquifers is essential to conduct reliable pre‐ and post‐risk assessments for site selection and operation, respectively. One of the primary challenges of performing such simulations is lack of adequate information about source conditions, such as hydro‐structural properties of caprock fracture/fault zone and the permeability field of the storage formation. This research investigates the impact of source condition uncertainties on the accuracy of leaking brine plume predictions. Prediction models should be able to simulate brine leakage and transport in complex multilayered geologic systems with interacting regional natural and leakage flows. As field datasets are not readily available for model testing and validation, three comprehensive intermediate‐scale laboratory experiments were used to generate high‐resolution spatiotemporal data on brine plume development under different leakage scenarios. Experimental data were used to validate a flow and transport model developed using existing code FEFLOW to simulate brine plume under varying source conditions. Spatial moment analysis was conducted to evaluate how uncertainty in source conditions impacts brine migration predictions. Results showed that inaccurately prescribing the permeability field of storage formation and caprock fractures in models can cause errors in leakage pathway and spread predictions up to ∼19% and ∼100%, respectively. These findings will help in selecting and characterizing storage sites by factoring in potential risks to shallow groundwater resources.
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- PAR ID:
- 10367582
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 57
- Issue:
- 9
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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