Abstract
Carbon dioxide (CO2) storage into geological formations is regarded as an important mitigation strategy for anthropogenic CO2 emissions to the atmosphere. This study first simulates the leakage of CO2 and brine from a storage reservoir through the caprock. Then, we estimate the resulting pressure changes at the zone overlying the caprock also known as Above Zone Monitoring Interval (AZMI). A data-driven approach of arbitrary Polynomial Chaos (aPC) Expansion is then used to quantify the uncertainty in the above zone pressure prediction based on the uncertainties in different geologic parameters. Finally, a global sensitivity analysis is performed with Sobol indices based on the aPC technique to determine the relative importance of different parameters on pressure prediction. The results indicate that there can be uncertainty in pressure prediction locally around the leakage zones. The degree of such uncertainty in prediction depends on the quality of site specific information available for analysis. The scientific results from this study provide substantial insight that there is a need for site-specific data for efficient predictions of risks associated with storage activities. The presented approach can provide a basis of optimized pressure based monitoring network design at carbon storage sites.
Highlights
Capture and geologic storage of carbon dioxide (CO2) is considered as one of a portfolio of solutions for the reduction of anthropogenic greenhouse gas emissions
We use the National Risk Assessment Partnership (NRAP) Seal Barrier reduced order models (ROMs), NSealR7 to compute the migration of CO2 and brine through the seal to overlying Above Zone Monitoring Interval (AZMI) formation through intrinsic permeability and/or the presence of natural/induced fractures in the seal
The large uncertainties in the AZMI ROM prediction makes it important to analyze the role of each individual parameter on the output space
Summary
Capture and geologic storage of carbon dioxide (CO2) is considered as one of a portfolio of solutions for the reduction of anthropogenic greenhouse gas emissions. Because full-physics numerical simulation models are computationally expensive, it may require several hours to days to complete a single, deterministic realization; as such, exploring uncertainty/variability in system performance using such models and brute-force Monte Carlo simulation is generally considered intractable[16,17,18,19,20] This makes it favorable to use advanced stochastic tools to model uncertainties of complicated processes involved in the geologic storage of carbon modeling. We use a recently developed data-driven uncertainty quantification approach, called the arbitrary polynomial chaos (aPC) expansion that provides a massive stochastic model reduction[15,21] to analyze the uncertainties in predictive ability of the AZMI ROM. The goal of this study is to probabilistically assess the role of various geologic parameters in AZMI pressure predictions
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