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Applicability of aquifer impact models to support decisions at CO2 sequestration sites

Abstract The National Risk Assessment Partnership has developed a suite of tools to assess and manage risk at CO2 sequestration sites. This capability includes polynomial or look-up table based reduced-order models (ROMs) that predict the impact of CO2 and brine leaks on overlying aquifers. The development of these computationally-efficient models and the underlying reactive transport simulations they emulate has been documented elsewhere ( Carroll et al., 2014a , Carroll et al., 2014b , Dai et al., 2014 , Keating et al., 2016 ). In this paper, we seek to demonstrate applicability of ROM-based analysis by considering what types of decisions and aquifer types would benefit from the ROM analysis. We present four hypothetical examples where applying ROMs, in ensemble mode, could support decisions during a geologic CO2 sequestration project. These decisions pertain to site selection, site characterization, monitoring network evaluation, and health impacts. In all cases, we consider potential brine/CO2 leak rates at the base of the aquifer to be uncertain. We show that derived probabilities provide information relevant to the decision at hand. Although the ROMs were developed using site-specific data from two aquifers (High Plains and Edwards), the models accept aquifer characteristics as variable inputs and so they may have more broad applicability. We conclude that pH and TDS predictions are the most transferable to other aquifers based on the analysis of the nine water quality metrics (pH, TDS, 4 trace metals, 3 organic compounds). Guidelines are presented for determining the aquifer types for which the ROMs should be applicable.
- Los Alamos National Laboratory United States
- Pacific Northwest National Laboratory United States
- Lawrence Berkeley National Laboratory United States
- Pacific Northwest National Laboratory United States
- Lawrence Berkeley National Laboratory United States
Carbon sequestration, Energy, Life on Land, Geology, 333, Environmental sciences, Earth sciences, Engineering, Reduced-order modeling, Earth Sciences, Hydrology, Environmental Sciences, Groundwater impacts, Risk assessment
Carbon sequestration, Energy, Life on Land, Geology, 333, Environmental sciences, Earth sciences, Engineering, Reduced-order modeling, Earth Sciences, Hydrology, Environmental Sciences, Groundwater impacts, Risk assessment
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