Faults serve as crucial pathways and sites for hydrocarbon migration and accumulation, making their identification a key task in the interpretation of seismic data. However, the diversity of fault types, extensive distribution, and complex characteristics pose significant challenges to fault identification. To address this issue, this paper proposes a fault identification method using a 3D TransUnet model. Constructed based on 3D CNN and transformer modules, this model adopts an end-to-end structural design of the 3D Unet architecture. By learning the spatial relationships of three-dimensional faults in synthetic seismic data, it directly predicts fault information in actual seismic data. The method has been successfully applied to seismic work areas in the F3 block of the Dutch North Sea and the Halahatang area of the Tarim Basin, achieving excellent results. The research findings demonstrate that the 3D TransUnet model combines the high local accuracy of CNN and the global attention mechanism of Transformer, enabling inference and prediction of faults in complex regions based on global fault information. Compared with the 3D Unet model and other traditional fault identification methods, the 3D TransUnet model achieved a recall rate of 0.87 and a precision rate of 0.83 on the validation set, significantly outperforming other approaches. In practical applications within three-dimensional seismic work areas, the 3D TransUnet model accurately identifies fault information across different regions. For faults with subtle features, the incorporation of the Transformer module equips the model with a global attention mechanism, allowing it to infer the presence of faults by analyzing distribution trends across the entire work area. By applying the trained fault identification model to different practical seismic work areas (the F3 block and the Halahatang area in this study), the universality of the method is demonstrated, indicating that the trained fault identification model can be effectively utilized across seismic data from various regions. This study finds that the method can effectively identify microfracture information within formations. In oil and gas fields where microfractures serve as reservoirs, since microfractures primarily develop along major faults, well locations are typically deployed near these large faults. However, during the middle and late stages of oil production in such fields, well placement decisions rely more heavily on the development degree of microfractures. Therefore, this fault identification method provides valuable guidance for well placement in oil and gas fields where microfractures act as reservoirs.

Recently, the great potential of seismic dispersion attributes in oil and gas exploration has attracted extensive attention. The frequency-dependent amplitude versus offset (FAVO) technology, with dispersion gradient as a hydrocarbon indicator, has developed rapidly. Based on the classical AVO theory, the technology works on the assumption that elastic parameters are frequency-dependent, and implements FAVO inversion using spectral decomposition methods, so that it can take dispersive effects into account and effectively overcome the limitations of the classical AVO. However, the factors that affect FAVO are complicated. To this end, we construct a unified equation for FAVO inversion by combining several Zoeppritz approximations. We study and compare two strategies respectively with (strategy 1) and without (strategy 2) velocity as inversion input data. Using theoretical models, we investigate the influence of various factors, such as the Zoeppritz approximation used, P- and S-wave velocity dispersion, inversion input data, the strong reflection caused by non-reservoir interfaces, and the noise level of the seismic data. Our results show that FAVO inversion based on different Zoeppritz approximations gives similar results. In addition, the inversion results of strategy 2 are generally equivalent to that of strategy 1, which means that strategy 2 can be used to obtain dispersion attributes even if the velocity is not available. We also found that the existence of non-reservoir strong reflection interface may cause significant false dispersion. Therefore, logging, geological, and other relevant data should be fully used to prevent this undesirable consequence. Both the P- and S-wave related dispersion obtained from FAVO can be used as good indicators of a hydrocarbon reservoir, but the P-wave dispersion is more reliable. In fact, due to the mutual coupling of P- and S-wave dispersion terms, the P-wave dispersion gradient inverted from PP reflection seismic data has a stronger hydrocarbon detection ability than the S-wave dispersion gradient. Moreover, there is little difference in using post-stack data or pre-stack angle gathers as inversion input when only the P-wave dispersion is desired. The real application examples further demonstrate that dispersion attributes can not only indicate the location of a hydrocarbon reservoir, but also, to a certain extent, reveal the physical properties of reservoirs.