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The frequency-dependent amplitude-versus-offset (FAVO) method has great potential for reservoir parameters estimation; however, it is hard work to establish the FAVO inversion model. It is also difficult to solve the inverse problem for FAVO by traditional methods. In this article, we propose a new workflow to extract the reservoir fluid parameters from the FAVO gathers based on a deep neural network (DNN). The proposed method is applied to predict the tight sandstone gas reservoir properties. Within the framework of this workflow, we generate the synthetic FAVO gathers. First, we establish the petrophysical model using the logging interpretation results. Then, the Backus average, Biot–Gassmann fluid substitution, velocity dispersion equations of the binary medium, and Rüger equation are applied to generate the FAVO reflectivity series. By introducing the DNN-based seismic wavelet estimation method and the optimal basis wavelet transform (OBWT), we can generate different frequency components of the seismic wavelet. These different frequency components are used to convolve the FAVO reflectivity series to obtain FAVO gathers that are used to generate the sample pairs for DNN training. At the same time, the OBWT is used to decompose the real AVO gathers to get the FAVO gathers. Finally, to test its validity and effectiveness, the proposed workflow is applied to synthetic and field data.

The fluid-saturated porous layered (FSPL) media widely exist in the Earth’s subsurface and their overall mechanical properties, microscopic pore structure and wave propagation characteristics are highly relevant to the in-situstress. However, the effect of in-situstress on wave propagation in FSPL media cannot be well explained with the existing theories. To fill this gap, we propose the dynamic equations for FSPL media under the effect of in-situstress based on the theories of poroacoustoelasticity and anisotropic elasticity. Biot loss mechanism is considered to account for the stress-dependent wave dispersion and attenuation induced by global wave-induced fluid flow. Thomsen’s elastic anisotropy parameters are used to represent the anisotropy of the skeleton. A plane-wave analysis is implemented on dynamic equations yields the analytic solutions for fast and slow P waves and two S waves. Modelling results show that the elastic anisotropy parameters significantly determine the stress dependence of wave velocities. Vertical tortuosity and permeability have remarkable effects on fast and slow P-wave velocity curves and the corresponding attenuation peaks but have little effect on S-wave velocity. The difference in velocities of two S waves occurs when the FSPL medium is subjected to horizontal uniaxial stress, and the S wave along the stress direction has a larger velocity, which implies that the additional anisotropy other than that induced by the beddings appears due to horizontal stress. Besides, the predicted velocity results have the reasonable agreement with laboratory measurements. Our equations and results are relevant to a better understanding of wave propagation in deep strata, which provide some new theoretical insights in the rock physics, hydrocarbon exploration and stress detection in deep-strata shale reservoirs.