The conventional coherence attribute is typically applied to migrated full-stack seismic data volumes to detect geologic discontinuities. Recently, multispectral, multiazimuth, and multioffset coherence attributes have been proposed and implemented with different seismic data volumes of specific frequencies, azimuths, and offsets to enhance discontinuities. Generally, geologic anomalies, such as faults and channels, will be better illuminated by a perpendicular rather than a parallel direction for computation. Therefore, we have developed a multidirectional eigenvalue-based coherence attribute by establishing multiple covariance matrices along certain different directions on a single poststack volume. We adopt two methods to compute a multidirectional coherence attribute. One is to compute multiple coherence volumes in different directions and to define the minimum as the final multidirectional coherence. This method is time-consuming, but it could provide partial and overall discontinuity simultaneously. The other method obtains one coherence volume by summing covariance matrices in different directions, which is computationally efficient, but only provides overall discontinuity. The performance of our 3D physical model and field data volumes demonstrates that multidirectional coherence can highlight subtle geologic structures with a higher resolution than conventional coherence. This suggests that a multidirectional coherence attribute may serve as an effective tool for detecting the distribution of geologic discontinuities in seismic interpretation.

As crucial storage spaces for underground hydrocarbon resources, karst caves are essential for the exploitation and development of carbonate-prone reservoirs. The precise characterization of karst caves enables efficient resource management and enhances the effectiveness of oil and gas exploration and development. Due to noise interference and discontinuities in geologic strata, traditional methods that rely on seismic properties to detect karst caves are highly subjective, prone to multiple solutions, and require extensive manual intervention, resulting in low development efficiency. With the growing demand for exploration, overcoming the limitations of traditional methods through more efficient and precise karst cave identification technologies has emerged as a critical focus in carbonate-prone reservoir research. To address these limitations in 3D karst cave identification, we introduce a variant architecture of TransUnet, which integrates transformer and dual attention mechanisms with a convolutional neural network while upgrading the convolutional module from two dimensions to three dimensions to describe better the location and boundary characteristics of the karst caves in field seismic data. Compared with traditional U-shaped architectures, the model offers a faster convergence rate (epochs <10) during training and fewer false predictions during validation with open-source synthetic data. Further applications of physical models and field seismic data provide more accurate details on the positions and boundaries of karst caves, demonstrating their significant potential to improve the accuracy and resolution of karst cave detection and effectively enhance the characterization of carbonate reservoirs.

As an approximation of the spherical-wave reflection coefficient (SRC), the plane-wave reflection coefficient does not fully describe the reflection phenomenon of a seismic wave generated by a point source. The applications of SRC to improve analyses of seismic data have also been studied. However, most of the studies focus on the time-domain SRC and its benefit to using the long-offset information instead of the dependency of SRC on frequency. Consequently, we have investigated and accounted for the frequency-dependent spherical-wave reflection coefficient (FSRC) and analyzed the feasibility of this type of inversion. Our inversion strategy requires a single incident angle using reflection data for inverting the density and velocity ratios, which is distinctly different from conventional inversion methods using amplitude variation with offset. Hence, this investigation provides an alternative approach for estimating media properties in some contexts, especially when the range of aperture of the reflection angles is limited. We apply the FSRC theory to the inversion of noisy synthetic and field data using a heuristic algorithm. The multirealization results of the inversion strategy are consistent with the feasibility analysis and demonstrate the potential of the outlined method for practical application.

Micro-pore structures are an essential factor for the electrical properties of porous rock. Theoretical electrical conductivity models considering pore structure can highly improve the accuracy of reservoir estimation. In this study, a pore structure characterization method based on a multi-fractal theory using capillary pressure is developed. Next, a theoretical electrical conductivity equation is derived based on the new pore structure characterization method. Furthermore, a distinct interrelationship between fractal dimensions of capillary pressure curves ($D_v$) and of resistivity index curves (${\mathrm{D}}_t$ and ${\mathrm{D}}_r$) is obtained. The experimental data of 7 sandstone samples verify that the fitting result by the new pore structure characterization method is highly identical to the experimental capillary pressure curves, and the accuracy of the improved rock resistivity model is higher than the Archie model. In addition, capillary pressure curves can be directly converted to resistivity index curves according to the relationship model between fractal dimensions of capillary pressure curves ($D_v$) and resistivity index curves (${\mathrm{D}}_t$ and ${\mathrm{D}}_r$). This study provides new ideas to improve the accuracy of pore structure characterization and oil saturation calculation; it has good application prospects and guiding significance in reservoir evaluation and rock physical characteristics research.

The accurate identification and characterization of faults in seismic data is a key step for geological structure interpretation, favorable traps and reservoirs, and recovery of hydrocarbons. Artificial fault marking is not only time-consuming and laborious but also influenced by seismic quality and interpretation experience. The current commonly used U-Net models have satisfactory performance in assisting fault interpretation, but which have limited ability to fine segmentation and recover lost feature information, due to the lack of representative datasets and limitations of network architecture. This paper alleviates this problem by optimizing network architecture, we propose a concise W-Net architecture to identify fault information by designing two expansive paths to obtain rich context information from different scales and enhance the learning ability of the neural network. In the skip connection part, we add double path semantic segmentation features to effectively transfer information, improve the positioning accuracy, optimize memory usage, extract important fault features, and suppress irrelevant information. The synthetic results show that the convergence rate of accuracy and loss of W-Net is faster than that of U-Net in the early stages (epoch<=10). The geologic detailed information of fault identification with W-Net in 3D actual seismic data is more abundant and continuous, which indicates that W-Net can serve as an improved tool to automatically characterize the distribution of geologic discontinuity in seismic images.