A novel fluid identification method based on a high-precision spectral decomposition method

Time-frequency decomposition technology is an effective tool for analyzing non-stationary signals. Improving resolution of spectral decomposition techniques is important to extract more useful information from the received signal. The Wigner-Ville distribution (WVD) has been widely applied in seismic signal analysis, it can better analyze seismic signals due to many excellent mathematical properties, but this method has a drawback that cross terms interference exists in the analyzing of multicomponent signals, which severely limits its application. The combination of the complex-domain matching pursuit (CDMP) with this approach effectively solves this problem. However, the conventional CDMP-WVD does not take the influence of the scale parameter on the Morlet wavelet waveform into account, which reduces the time-frequency resolution of CDMP-WVD. Therefore, to correct the defect that the atomic waveforms change only with the frequency parameter, we propose an improved spectral decomposition method ICDMP-WVD that considers the scale parameter. In this study, we first analyze influences of the scale parameter on Morlet wavelet waveform and make the scale parameter a search parameter that improves the computational efficiency and time-frequency resolution of the traditional CDMP-WVD method. Accordingly, the seismic dispersion-dependent attributes are calculated via combing the improved CDMP-WVD algorithm and the frequency-dependent AVO inversion. We adopt a two-step frequency-dependent AVO inversion method to improve the stability of the conventional frequency-dependent AVO inversion. Theoretical data and real data application show that the approach in this study can identify gas reservoirs efficiently and accurately.