Using subsurface CO2 concentrations and isotopologues to identify CO2 seepage from CCS/CO2–EOR sites: A signal-to-noise based analysis

Abstract In order to fulfill a role in demonstrating containment, surface monitoring for Carbon Capture and Geologic Storage (CCS) sites must be able to clearly discriminate between natural, and reservoir-source CO 2 . The CCS community lacks a clear metric for quantifying the degree of discrimination, for successful inter-comparison of monitoring approaches. This study illustrates the utility of signal-to-noise ratio (SNR) to compare the relative performance of three commonly used soil gas monitoring approaches, including bulk CO 2 , δ 13 CO 2 , and Δ 14 CO 2 . For inter-comparisons, we used a simulated northern temperate landscape similar to that of Weyburn, Saskatchewan (home of the IEAGHG Weyburn–Midale CO 2 Monitoring and Storage Project), in which realistic spatial and temporal CO 2 and isotopic variation is simulated over multiple annual cycles, so that the techniques may also be inter-compared seasonally. Results show that methods with strong difference between biological and seepage source CO 2 , such as Δ 14 C signatures in this study, have the best overall SNR values. However, our analysis also shows each monitoring approach could be useful, depending on the desired seepage certainty level and characteristics including site spatial variability and injection gas attributes. This study emphasizes both the importance of developing clear metrics for monitoring performance, the need to evaluate SNR and MMV approaches on a site specific basis, and the benefit of modeling for decision support in CCS monitoring design.