Assessing the seismic AVA detectability of CO2 storage sites using novel time-lapse attributes

Monitoring of stored carbon dioxide (CO2) in subsurface reservoirs is fundamental to operation and management of the storage site, and is a requirement of some national and international legislation. As a consequence, effectiveness of monitorability (the ability to observe the evolving location of subsurface CO2) for any given level of investment in monitoring technology is a significant investment uncertainty that must be assessed along other components of the storage-site selection criteria (e.g. capacity, injectivity and storage economies). We develop a workflow to assess the time-lapse seismic detectability of changes in subsurface aquifer reservoirs by analysing expected changes in seismic amplitude variation with angle (AVA) in the field. Laboratory measurements are used to calculate the seismic response of the reservoir at different saturations and pressures. We include the scattering effect of material above and below the reservoir by using a finite-difference, full-waveform modelling approach AVA analysis then assimilates local site effects into the detectability assessment. We show that performing waveform modelling which includes local geological heterogeneities above and below the reservoir interval is essential to assess the storage site monitroability. In order to quantify expected time-lapse changes in the seismic response, we introduce a new set of robust time-lapse attributes based on time–frequency decomposition. The attributes effectively separate amplitude and phase changes (time-shifts) of time-lapse seismic records, and allow us to quantify their repeatability against the background noise. Furthermore, the frequency-dependent nature of the attributes provides a quantification of the frequency–domain effects of time-lapse changes. The approach is employed to assess the detectability of supercritical CO2 in two analogue storage sites in the near-shore UK North Sea. Analysis of laboratory measurements and AVA responses indicate the contrasting monitorability of the two sites, which helps decision making about further site investigation and development. Application of the approach to hydrocarbon reservoir monitoring is straightforward.