The normal-to-shear fracture compliance ratio is commonly used as a fluid indicator. In the seismic frequency range, the fluid indicator lies between the values for isolated fluid-filled fractures and dry fractures, and it is not easy to discriminate the fluid content. Assuming that the fracture surfaces are smooth, we use $1-{\mathrm{\Delta }}_N/{\mathrm{\Delta }}_N^{\mathrm{dry}}$, with ${\mathrm{\Delta }}_N$ and ${\mathrm{\Delta }}_N^{\mathrm{dry}}$ representing the normal fracture weakness of the saturated and dry rock, to indicate fluid types, and to define a fluid influencing factor. The fluid influencing factor is sensitive to the fluid properties, the aspect ratio of the fractures, and the frequency. Conventionally, the amplitude versus offset and azimuth (AVOA) inversion is formulated in terms of the contrasts of the fracture weaknesses across the interface, assuming that the fractures are vertical with the same symmetry axis. We consider fractures with arbitrary azimuths, and develop a method to estimate fracture parameters from wide-azimuth seismic data. The proposed AVOA inversion algorithm is tested on real 3D prestack seismic data from the Tarim Basin, China, and the inverted fracture density show good agreement with well log data, except that there are some discrepancies for one of the fractured reservoir sections. The discrepancies can be ascribed to neglect of the dip angle for the tilted fractures and the conjugate fracture sets, and to the validity of the linear-slip model. The fractured reservoirs are expected to be liquid saturated, under the assumption of smooth fractures. Overall, the inverted fracture density and fluid influencing factor can be potentially used for better well planning in fractured reservoirs and quantitatively estimating the fluid effects.

Spectral decomposition has been widely used to detect frequency-dependent anomalies associated with hydrocarbons. By ignoring the time-variant feature of the frequency content of individual reflected wavelets, we have adopted a sparse time-frequency spectrum and developed a matching pursuit-based sparse spectral analysis (MP-SSA) method to estimate the sparse time-frequency representation of the seismic data. Further, we evaluate a generalized nonstationary convolution model concerning propagation attenuation and frequency-dependent reflectivity, and we mathematically evaluate the sparse time-frequency spectrum of the nonstationary seismic data as being equal to the product of the Fourier spectrum of the source wavelet, frequency-dependent reflection coefficient, and the cumulative attenuation during seismic wave propagation. Therefore, the reflectivity spectrum, which is a combination of the frequency-dependent reflectivity and the propagation attenuation, can be determined by dividing the sparse time-frequency spectrum of the seismic data by the Fourier spectrum of the source wavelet. Application of the matching pursuit-based decomposition methods to synthetic nonstationary convolutional data illustrates that the adopted MP-SSA spectrum shows a higher time resolution than the matching pursuit-based Wigner-Ville distribution and the matching pursuit-based instantaneous spectral analysis spectra. Notably, the MP-SSA method can avoid spectral smearing, which may introduce distortions to the frequency-dependent anomaly estimation. Application of the amplitude versus frequency analysis based on MP-SSA to field data illustrates the potential of using the sparse reflectivity spectral intercept and gradient to detect the hydrocarbon reservoirs.

Seismic deconvolution aims to improve the vertical seismic resolution by compressing the seismic wavelet and extending the seismic frequency bandwidth. Deconvolution in the frequency domain is normally implemented in three steps: wavelet estimation, deconvolution operator construction, and data filtering. We propose a seismic resolution enhancement approach by spectral shaping using shaping-regularized inversion. Instead of designing a deconvolution operator based on the extracted wavelet, we formulate an inverse problem using the seismic spectrum as the diagonal kernel matrix and a predefined filter as the expected spectrum. Assuming the randomness in the reflectivity and smoothness of the spectral shaping operator, we estimate the spectral shaping operator by inversion via a shaping regularization scheme, which imposes constraints by shaping the estimated spectral shaping operator model to the admissible model space. The role of the shaping regularization in inversion is to ensure the continuity and smoothness of the spectral shaping operator. We use a synthetic model and real land seismic data to demonstrate the effectiveness of the proposed approach in seismic resolution enhancement.

The scarcity and incompleteness of well-log data pose a critical challenge to deep learning-based reservoir prediction. To address this small-sample problem and improve prediction quality, we propose a novel semi-supervised asymmetric deep co-training framework integrated with a shape context descriptor. This method leverages abundant unlabeled seismic data as well as complementary information on related physical properties. Specifically, we introduce a shape context descriptor to encode seismic waveform morphology and spatial context, thereby improving the lateral continuity and interpretability of predictions while mitigating issues inherent in the sequence-to-point paradigm, wherein three-dimensional seismic data are used as input and a single target point is predicted. To overcome data limitations, a sliding-window resampling strategy is employed to expand the training samples. For co-training, we design an asymmetric dual-task architecture wherein one model performs porosity regression while the other conducts reservoir type classification, thereby enabling synergistic learning. The proposed framework is validated using real three-dimensional seismic data from the Yinggehai Basin in the South China Sea through ablation experiments. The results demonstrate superior performance in prediction accuracy, spatial consistency, and training stability compared to baseline methods.