Sedimentary cycle analysis plays an important role in stratigraphic theory research and petroleum exploration and development.Qualitative division of logging curves is a conventional method for dividing sedimentary cycles,which is more accurate at wellbore locations,but the division of sedimentary cycles in non-wellbore locations mainly depends on geological knowledge and has strong subjectivity.Seismic data contain abundant information related to sedimentary cycles.Sedimentary cycles can be divided by using time-frequency attribute curves.In this paper,the generalized S-transform method with better time-frequency resolution is used to calculate the time-frequency attribute curve,and the method is applied to four sedimentary cycle models:normal cycle,inverse cycle,normal-inverse cycle and inverse-normal cycle.The results of cycles division of the model validate the validity of the peak frequency attributes of time-frequency spectrum in the division of sedimentary cycles;However,in the practical application of seismic data,the time resolution of time-frequency spectrum is not high.Teager-Kaiser energy spectrum based on time-frequency spectrum improves the time positioning and focusing of time-frequency analysis results.The division of sedimentary cycles based on Teager-Kaiser energy spectrum attributes can better depict the changes of geological structure and thin interbedded structure.The method is applied to the Jurassic sedimentary cycle division of an oil field in Xinjiang.The results of the division are in good agreement with those of the well data,which verifies the reliability of the method.
Chen X H, He Z H, Huang D J. High efficient time-frequency spectrum decomposition of seismic data based on generalized S transform[J]. OGP, 2008,43(5):530-534.
Teager H, Teager S. Evidence for nonlinear production mechanisms in the vocal tract[J]. Speech Production & Speech Modeling, 1990,55:241-261.
Kaiser J F. On a simple algorithm to calculate the 'energy' of a signal[J]. IEEE ICASSP, 1990: 381-384.
Jabloun F, Cetin A E, Erzin E. Teager energy based feature parameters for speech recognition in car noise[J]. IEEE Signal Processing Letters, 1999,6(10):258-261.
Nehe N S, Holamber R S. Power spectrum difference teager energy features for speech recognition in noisy environment[C]// IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, 2008: 1-5.
Matos D, Marfurt K J. Wavelet transform Teager-Kaiser energy applied to a carbonate field in Brazil[J]. The Leading Edge, 2009,28(6):708-713.
Gao Y J. Sedimentary cycle division and correlation of sand-conglomerate body in upper Sha IV Formation of Yanjia area, Jiyang depression[J]. Petroleum Geology and Recovery Efficiency, 2010,17(6):6-11.