澳门银河赌场注册送38-澳门银河赌场招人好招吗_百家乐德州_全讯网下载 (中国)·官方网站

Abstract: 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.