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Acoustic, elastic and poroelastic simulations of CO2 sequestration crosswell monitoring based on spectral-element and adjoint methods

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SUMMARY

The key issues in CO2 sequestration involve accurate monitoring, from the injection stage to the prediction and verification of CO2 movement over time, for environmental considerations. ‘4-D seismics’ is a natural non-intrusive monitoring technique which involves 3-D time-lapse seismic surveys. Successful monitoring of CO2 movement requires a proper description of the physical properties of a porous reservoir. We investigate the importance of poroelasticity by contrasting poroelastic simulations with elastic and acoustic simulations. Discrepancies highlight a poroelastic signature that cannot be captured using an elastic or acoustic theory and that may play a role in accurately imaging and quantifying injected CO2. We focus on time-lapse crosswell imaging and model updating based on Fréchet derivatives, or finite-frequency sensitivity kernels, which define the sensitivity of an observable to the model parameters. We compare results of time-lapse migration imaging using acoustic, elastic (with and without the use of Gassmann's formulae) and poroelastic models. Our approach highlights the influence of using different physical theories for interpreting seismic data, and, more importantly, for extracting the CO2 signature from seismic waveforms. We further investigate the differences between imaging with the direct compressional wave, as is commonly done, versus using both direct compressional (P) and shear (S) waves. We conclude that, unlike direct P-wave traveltimes, a combination of direct P- and S-wave traveltimes constrains most parameters. Adding P- and S-wave amplitude information does not drastically improve parameter sensitivity, but it does improve spatial resolution of the injected CO2 zone. The main advantage of using a poroelastic theory lies in direct sensitivity to fluid properties. Simulations are performed using a spectral-element method, and finite-frequency sensitivity kernels are calculated using an adjoint method.

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