Thin layer identification method of prestack seismic data based on an improved 2D Teager-Huang transform

Abstract

Thin layer identification is significant in reservoir prediction, especially in deep and ultradeep reservoirs. Due to the weak effective signals of deep reservoirs, the effectiveness of the current thin layer positioning method is significantly reduced. Therefore, an improved 2D Teager-Huang transform (2D-THT) operator based on prestack seismic data is developed in this paper to identify and locate deep thin reservoirs. First, we combine bidimensional empirical mode decomposition (BEMD) and ensemble empirical mode decomposition (EEMD) to form the bidimensional ensemble empirical mode decomposition (BEEMD) algorithm. This BEEMD algorithm can effectively avoid the mode mixing problem of BEMD and efficiently decompose gathers to separate and extract the critical information. Then, we introduce a 2D Teager-Kaiser energy operator (2D-TKEO) to enhance the lateral and longitudinal instantaneous change characteristics of the selected effective components and focus on the weak reflection signal. Finally, the Hilbert transform is used to obtain the prestack spectral decomposition results, and the time–frequency superposition spectrum is obtained through superposition. The positions of the thin layer top and bottom are obtained by locating the frequency change point. This method uses the intrinsic signal change to obtain a strong sensitivity to thin layers to achieve thin reservoir positioning research. Synthetic data and actual data application examples of deep carbonate rocks in western Sichuan show that a method complementary to the existing thin layer detection methods is proposed in this paper and that good results on low signal-to-noise ratio data are achieved.