Acoustic impedance (AI) reflects the physical properties of subsurface materials and is a key parameter in reservoir evaluation and hydrocarbon exploration. Traditional poststack AI inversion methods typically assume a time-invariant wavelet, neglecting the nonstationary effects caused by attenuation and absorption during wave propagation. Therefore, performing attenuation compensation prior to inversion can effectively enhance seismic recording resolution and improve inversion accuracy. Nonetheless, attenuation compensation frequently encounters challenges such as incomplete correction and the amplification of noise. In this context, impedance inversion of nonstationary seismic records that accounts for attenuation effects can effectively avoid cumulative errors and noise amplification caused by attenuation compensation, thereby enhancing both the efficiency and accuracy of the inversion process. Existing nonstationary inversion methods, typically based on constant-Q models and reliant solely on time-domain information, struggle to handle realistic frequency-dependent Q variations. To address these limitations, we propose a poststack acoustic impedance inversion method in the time-frequency mixed domain, applicable to frequency-dependent Q attenuation. First, we derive a nonstationary convolution model in both time and frequency domains based on the frequency-dependent Q model. Then, we construct an impedance inversion objective function in the time-frequency mixed domain and solve it using the alternating direction method of multipliers (ADMM). The ADMM algorithm enables efficient iterative optimization by decomposing the objective into subproblems and solving them alternately. Tests on synthetic and field data demonstrate that the proposed method can effectively perform AI inversion from nonstationary seismic records with frequency-dependent Q attenuation. By incorporating frequency-domain information, it enhances the accuracy and resolution of the AI inversion.

Description of fluid properties, natural fractures, and stress conditions is significant for seismic characterization of unconventional fractured reservoirs. For the first two aspects, traditional methods to solid-liquid decoupling by low-frequency anisotropic Gassmann fluid substitution equation fall short in two aspects: 1) neglecting the combined effects of fractures and pore fluid, and 2) disregarding the influence of fracture inclination on the seismic anisotropy. To address the above two issues simultaneously, this article derives a novel PP-wave reflection coefficient incorporating fluid bulk modulus, vertical effective stress correlation parameter, fracture density, and coupled anisotropic fluid indicator (CFI) in the tilted transverse isotropy (TTI) medium. The derived TTI-saturated stiffnesses with CFI are revealed to provide the improved accuracy regarding physical property parameters (porosity, fluid fillings, and matrix mineral content) and fracture parameters (including fracture density and inclination). To invert the above key parameters from offset vector tile (OVT) domain seismic data, the stepwise seismic inversion strategy based on Lp quasi-norm sparsity constraints is employed. Synthesized azimuthal seismic data with varying signal-to-noise ratios (SNR) validate the feasibility and robustness of the proposed inversion method. Ultimately, the innovative method exhibits compelling effectiveness when applied to fractured shale gas-bearing reservoirs in the Sichuan Basin, China.

Presents corrections to the paper, (Corrections to “Seismic Inversion of Anisotropic and Isotropic Fluid Indicators and Fracture Density in the TTI Medium”).

Viscoelastic attenuation occurs when seismic waves propagate through subsurface media, significantly reducing the signal-to-noise ratio (SNR) and resolution, which severely impacts the application and interpretation of seismic records. Inverse Q filtering (IQF) compensates for this attenuation via the method of wavefield continuation. The current IQF algorithms use the constant Q model, which assumes a frequency-independent Q. However, some field data and laboratory measurements indicate that Q is a function of frequency and reveal the limitations in the traditional IQF. In this article, we propose inverse frequency-dependent Q filtering (IFQF) based on the power-law Q model. In this method, the phase compensation is unconditionally stabilized, whereas the amplitude compensation is numerically unstable. Thus, stabilization is achieved by incorporating a stabilization factor. The experimental results on synthetic and field data show that the conventional IQF ignores the frequency dependence of the Q value, potentially leading to overcompensation. IFQF effectively compensates for phase dispersion and amplitude attenuation induced by the frequency-dependent Q attenuation, which significantly improves the resolution of seismic recordings.

During the propagation of seismic waves in the Earth, the influence of Earth absorption leads to energy attenuation and phase distortion. Traditional seismic acoustic impedance (AI) inversion typically utilizes a constant wavelet to construct a wavelet library for AI inversion, resulting in outcomes that struggle to effectively portray the spatial variation characteristics of underground reservoirs. While using an inverse Q filter for energy compensation before AI inversion can enhance precision, accurately extracting the Q factor before the inverse Q filter is complex. Moreover, the inverse Q filter often amplifies noise interference, impacting the accuracy of the inversion. Therefore, we propose an alternating iterative synchronous inversion method for AI and the quality factor. Compared with the conventional method, the impedance information and attenuation parameters of seismic data can be obtained directly, which improves the efficiency and accuracy of inversion. The method can be divided into two steps. Firstly, we construct the L1-2-norm regularization of the AI inversion objective function, which is solved using the difference of convex algorithm (DCA) and the alternating direction method of multipliers (ADMM). Subsequently, the Markov Chain Monte Carlo (MCMC) method is employed to solve the nonstationary forward equation, with the fitting of the centroid frequency of seismic data and synthetic seismic records. Through continuous iteration of AI and the quality factor until the error is below the specified threshold or reaches the designated number of iterations, stable AI and average Q factors are obtained. Numerical simulations demonstrate the reliability of the proposed method, showcasing higher accuracy compared to stable wavelet inversion and constant Q inversion. Real data applications validate the method’s effectiveness, providing more dependable results for reservoir fluid identification. The obtained average Q factors can be employed for seismic data processing and interpretation.

Seismic wave acquisition is usually disturbed by natural noise and instrument noise. As the seismic wave propagates, the filtering effect of the Earth and its various layers will result in energy attenuation and velocity dispersion; these phenomena weaken the seismic time series amplitudes and distort the seismic phase data. In traditional processing methods, noise removal precedes the data compensation process, which attempts to retrieve information that was originally lost due to signal attenuation. If denoising is performed, weak seismic signals are often removed, resulting in the loss of useful signal data that cannot be recovered. Here, we propose a sparse regularization strategy based on dictionary learning. This inversion method performs the denoising and data compensation tasks in parallel. By applying this method to both synthetic and real datasets, we demonstrate that this technique effectively compensates and denoises the seismic data and improves both the data resolution and the signal-to-noise ratio of the seismic records of interest.

The inversion of reservoir elastic parameters and fracture parameters is of great significance to oil and gas production. In shale reservoirs with inclined fractures, using the reflection coefficient equation of vertical fractures or horizontal fractures under the vertical transverse isotropy (VTI) background has certain limitations. For this reason, based on the linear-slip model, this article establishes the approximate stiffness matrix of the monoclinic medium with a set of inclined fractures under the background of VTI and combines the Born scattering theory to further derive the PP-wave linear reflection coefficient equation of the monoclinic medium. The equation includes parameters, such as Young’s modulus, Poisson’s ratio, density, fracture weaknesses, and bedding weaknesses. Then, a rock physics model that comprehensively considers horizontal bedding and inclined fractures is established, and the influence of the inclination of the fractures on the reflection coefficient is analyzed. Finally, taking the advantage of Bayesian theory, the fracture weakness inversion method based on the azimuth amplitude difference is established, and the inversion accuracy is improved by adding Cauchy constraints and smoothing model regularization terms. Then, take the fracture weaknesses as inputs to invert the parameters, such as Young’s modulus, Poisson’s ratio, density, and bedding weaknesses. The inversion results of theoretical data show that the method can invert the fracture information well, and it can also be effectively applied to low-to-medium signal-to-noise ratio (SNR) data. The inversion results of theoretical and actual data prove that the inversion equation derived in this article can be used to obtain more accurate elastic and fracture parameters in fractured shale reservoirs.