Dynamics of geologic CO ₂ storage and plume motion revealed by seismic coda waves

Abstract

Quantifying the dynamics of sequestered CO2 plumes is critical for safe long-term storage, providing guidance on plume extent, and detecting stratigraphic seal failure. However, existing seismic monitoring methods based on wave reflection or transmission probe a limited rock volume and their sensitivity decreases as CO2 saturation increases, decreasing their utility in quantitative plume mass estimation. Here we show that seismic scattering coda waves, acquired during continuous borehole monitoring, are able to illuminate details of the CO2 plume during a 74-h CO2 injection experiment at the Frio-II well Dayton, TX. Our study reveals a continuous velocity reduction during the dynamic injection of CO2, a result that augments and dramatically improves upon prior analyses based on P-wave arrival times. We show that velocity reduction is nonlinearly correlated with the injected cumulative CO2 mass and attribute this correlation to the fact that coda waves repeatedly sample the heterogeneous distribution of cumulative CO2 in the reservoir zone. Lastly, because our approach does not depend on P-wave arrival times or require well-constrained wave reflections it can be used with many source–receiver geometries including those external to the reservoir, which reduces the risk introduced by in-reservoir monitoring wells. Our results provide an approach for quantitative CO2 monitoring and plume evolution that increases safety and long-term planning for CO2 injection and storage.

Preseismic Fault Creep and Elastic Wave Amplitude Precursors Scale With Lab Earthquake Magnitude for the Continuum of Tectonic Failure Modes

“…Active and passive seismic monitoring techniques have proved promising particularly in the realm of reservoir monitoring (Lumley, ; Zhu et al, ), field studies of postseismic healing (Brenguier et al, ), and laboratory studies of fault frictional state (Nagata et al, ; Shreedharan et al, ; Yoshioka & Iwasa, ) and coseismic energy release (Aichele et al, ; Latour et al, ). The use of acoustic amplitude (or acoustic transmissivity [AT]; see Supporting Information S1) is particularly appealing here since it has been demonstrated from theory and experiments (Kendall & Tabor, ; Kilgore et al, 2017; Nagata et al, ; Pyrak‐Nolte et al, ; Saltiel et al, ; Shreedharan et al, ) that AT across frictional interfaces is related to the stiffness and size of asperity contact junctions participating in shear.…”

Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes

“…Although a simplistic traditional approach is applied here, we believe that the example highlights the difficulty in seismic inversion using the traditional approaches without significant constraints. As mentioned in section , traditional RPMs do not resolve CO saturation well above a certain threshold (Zhu et al, ), making CO mass accounting uncertain. Our CycleGAN workflow provides a potential way for refining the saturation resolution of 4‐D seismic inversion through forward modeling, which is also a key contribution.…”

“…In this work, CycleGAN is used for 4-D seismic data inversion in the context of CO 2 plume tracking. We show that the proposed CycleGAN can be used to estimate the reservoir fluid property (e.g., CO 2 saturation) changes using 4-D seismic data at a high accuracy and efficiency, as compared to some traditional methods that may be insensitive to CO 2 saturation beyond a certain threshold (Zhu et al, 2019). The seismic impedance such as acoustic impedance (AI) can be calculated for each reservoir model grid cell, forming an image that has the same spatial dimensions as the reservoir property image (i.e, on the same grids).…”

Modeling Frequency‐Independent Q Viscoacoustic Wave Propagation in Heterogeneous Media

“…In the last example, we model the spatiotemporal attenuation effects on seismic waveforms by performing the time‐lapse wavefield simulations in a CO 2 injection geological model derived from the Frio‐II brine pilot CO 2 injection experiment (Daley et al, ; Zhu et al, , ). The time‐lapse models of both velocity and Q are retrieved from the Frio‐II CO 2 flow simulations using the rocks physics White's model (Daley et al, ).…”