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Shallow groundwater resources overlaying deep saline formations used in carbon storage applications are subjected to a potential contamination threat by CO2/brine leakage via natural or anthropogenically-induced conductive pathways in the confining caprock. Identifying the leakage source location and rate is critical for developing remediation plans and designing corrective actions. Owing to limited information about the flow and transport characteristics of deep regimes and high cost of obtaining data on their response to CO2 injection operation, estimating accurate source settings (i.e., location and rate) can be extremely challenging. Under such conditions, Bayesian inverse frameworks become useful tools to help identify potential leakage patterns. This study tests and validates an ensemble-based data-assimilation approach that reduces the uncertainty in the prior knowledge about source settings through conditioning forward transport models using relatively inexpensive easy-to-acquire shallow zone data. The approach incorporates the newly developed ensemble smoother tool in the inversion code “PEST++” with the transport code “FEFLOW” to perform history matching and uncertainty analysis. A novel parameterization method that allows the disposition of potential source was used to search the leakage location during calibration process. In the absence of field data, the approach was validated using experimental data generated in ~8 m long soil tank simulating leakage from storage zone migrating to the shallow aquifer. The results show that source location uncertainty can be reasonably reduced using shallow zone data collected from near-surface aquifers. However, more prior information about the system and deeper data are essential to estimate a practical probability range for the leakage rate.
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Monitoring Brine Leakage From Deep Geologic Formations Storing Carbon Dioxide: Design Framework Validation Using Intermediate‐Scale Experiment
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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- Award ID(s):
- 1702060
- PAR ID:
- 10370679
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 57
- Issue:
- 12
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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