Seismic monitoring of CCS with active and passive data: A synthetic feasibility study based on Pelican site, Australia

Abstract

Carbon capture and storage (CCS) is forecast to play a significant role towards CO${}_2$ emissions reduction. Cost-effective and simplified monitoring will be essential for rapid adoption and growth of CCS. Seismic imaging methods are regularly utilized to monitor low-velocity anomalies generated by injection of CO${}_2$ in the subsurface. In this study we generate active and passive synthetic seismic datasets at different stages of CO${}_2$ injection in the subsurface based on geologically constrained subsurface models of the Pelican storage site in the Gippsland Basin, Australia. We apply full waveform inversion (FWI) and wave-equation dispersion (WD) inversion to seafloor deployed distributed acoustic sensing (DAS) data to reconstruct the low-velocity anomalies. We model both strain (DAS) and displacement datasets for the active data component of the study and show that they result in similar reconstruction of the CO${}_2$ anomaly. FWI based time-lapse imaging of active data yields the most accurate results. However, this approach is expensive and also suffers from complex issues because of the near-onshore location of the storage site. Alternatively inverting passive data results in only minor differences, but can still effectively monitor changes in the subsurface, and assist in monitoring the CO${}_2$ plume at the reservoir depth. Furthermore, we demonstrate the capability of WD for inverting Scholte-waves derived from ambient noise for shallow detection of CO${}_2$ in the unlikely event of a leakage. Therefore, we propose a mixed mode monitoring strategy where passive data is utilized for routine monitoring while active surveys are deployed only when further investigation is required.