Conventional seismic analysis in partially saturated rocks normally lays emphasis on estimating pore fluid content and saturation, typically ignoring the effect of mobility, which decides the ability of fluids moving in the porous rocks. Deformation resulting from a seismic wave in heterogeneous partially saturated media can cause pore fluid pressure relaxation at mesoscopic scale, thereby making the fluid mobility inherently associated with poroelastic reflectivity. For two typical gas‐brine reservoir models, with the given rock and fluid properties, the numerical analysis suggests that variations of patchy fluid saturation, fluid compressibility contrast, and acoustic stiffness of rock frame collectively affect the seismic reflection dependence on mobility. In particular, the realistic compressibility contrast of fluid patches in shallow and deep reservoir environments plays an important role in determining the reflection sensitivity to mobility. We also use a time‐lapse seismic data set from a Steam‐Assisted Gravity Drainage producing heavy oil reservoir to demonstrate that mobility change coupled with patchy saturation possibly leads to seismic spectral energy shifting from the baseline to monitor line. Our workflow starts from performing seismic spectral analysis on the targeted reflectivity interface. Then, on the basis of mesoscopic fluid pressure diffusion between patches of steam and heavy oil, poroelastic reflectivity modeling is conducted to understand the shift of the central frequency toward low frequencies after the steam injection. The presented results open the possibility of monitoring mobility change of a partially saturated geological formation from dissipation‐related seismic attributes.

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The conventional convolutional model is widely applied to generate synthetic seismic data for numerous applications including amplitude-versus-offset forward modeling, seismic-well tie, and inversion. This approach assumes frequency-independent reflection coefficients and time-invariant seismic wavelets in laterally homogeneous elastic media. We have extended the conventional convolutional model to heterogeneous poroelastic media in which reflection coe?icients are frequency-dependent and the seismic wave gets attenuated as it propagates. First, we decompose the seismic wavelet into mono-frequency components through Fourier transform. Then, to account for the attenuation effects at the reflection interfaces, we multiply the frequency-dependent reflection coe?icients series with an attenuation function of frequency-variant quality factor Q. Finally, we convolve the above product results with a mono-frequency wavelet and sum all the frequencies together to obtain the synthetic seismograms. The advantage of the proposed frequency-decomposed nonstationary convolutional model is that it takes into account the effects of attenuation on both wave reflections and propagation in attenuative media. In addition, it employs the frequency-dependent Q instead of the constant Q utilized by the traditional nonstationary convolutional model. The technique has been applied to amplitude-versus-angle-and-frequency forward waveform modeling in attenuative media, and it shows good agreement between synthetic and real data on seismic-well ties.

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