Multisynchrosqueezing Generalized S-Transform and Its Application in Tight Sandstone Gas Reservoir Identification

被引:21
|
作者
Chen, Xuping [1 ,2 ]
Chen, Hui [1 ,2 ]
Li, Rui [2 ,3 ]
Hu, Ying [2 ,3 ]
Fang, Yuxia [2 ,3 ]
机构
[1] Chengdu Univ Technol, State Key Lab Oil & Gas Reservoir Geol & Explorat, Chengdu 610059, Peoples R China
[2] Chengdu Univ Technol, Geomath Key Lab Sichuan Prov, Chengdu 610059, Peoples R China
[3] Chengdu Univ Technol, Coll Informat Sci & Technol, Chengdu 610059, Peoples R China
基金
中国国家自然科学基金;
关键词
Reservoirs; Transforms; Estimation; Time-frequency analysis; Noise measurement; Geology; Continuous wavelet transforms; Instantaneous frequency (IF); multisynchrosqueezing generalized S-transform (GST); synchrosqueezing transform (SST); tight sandstone gas reservoirs; time-frequency (TF) analysis (TFA); TIME-FREQUENCY; SYNCHROSQUEEZING TRANSFORM; ATTENUATION;
D O I
10.1109/LGRS.2020.3043047
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Synchrosqueezing transform (SST) is a high-resolution time-frequency (TF) analysis (TFA) approach for seismic spectral anomaly detection. Here, a novel method called multisynchrosqueezing generalized S-transform (GST) is proposed and applied for the identification of tight sandstone gas reservoirs. In this method, a signal model named the Gaussian-Modulated Signal Model (GMSM) is introduced to estimate the instantaneous frequency (IF) of the signal in the GST's spectrum. Then, an iterative algorithm constantly approximating IF is constructed to provide a highly energy-concentrated TF representation while allowing for signal reconstruction. Compared to some advanced TFA methods, the proposed method has better energy-concentrated performance due to an accurate estimate of the IF. A simulated signal and field data are employed to verify the effectiveness of the proposed method. It is concluded that the proposed method has great potential as a TFA technique for identifying tight sandstone gas reservoirs.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Generalized fractional S-transform and its application to discriminate environmental background acoustic noise signals
    D. Jhanwar
    K. K. Sharma
    S. G. Modani
    Acoustical Physics, 2014, 60 : 466 - 473
  • [32] Identification of thin gas reservoir in reflection seismic data by synchrosqueezing S-transform in time-frequency representation
    Soheil Paksima
    Mohammad Radad
    Amin Roshandel Kahoo
    Mehrdad Soleimani Monfared
    Arabian Journal of Geosciences, 2023, 16 (6)
  • [33] A new evaluation method for recoverable reserves of water-bearing tight sandstone gas reservoir and its application
    Hu Y.
    Li X.
    Xu X.
    Mei Q.
    Chen Y.
    Wang J.
    Jiao C.
    Guo C.
    Jia Y.
    Shiyou Xuebao/Acta Petrolei Sinica, 2021, 42 (03): : 332 - 340
  • [34] Time-reassigned multisynchrosqueezing of the S-transform for seismic time-frequency analysis
    Liu, Wei
    Liu, Yang
    Li, Shuangxi
    ACTA GEOPHYSICA, 2024, 72 (02) : 759 - 775
  • [35] A new method of multi-scale fracture identification in tight gas sandstone reservoir
    Wu, Tao
    Wang, Yuezhi
    Fu, Bin
    Wu, Peng
    GEOSYSTEM ENGINEERING, 2019, 22 (02) : 112 - 118
  • [36] A Multisynchrosqueezing-Based S-Transform for Time-Frequency Analysis of Seismic Data
    Liu, Wei
    Zhai, Zhixing
    Fang, Zhou
    PURE AND APPLIED GEOPHYSICS, 2024, : 1279 - 1295
  • [37] Influencing Factors and Application of Spontaneous Imbibition of Fracturing Fluids in Tight Sandstone Gas Reservoir
    Zhang, Xueping
    Liu, Youquan
    Liu, Yuzhou
    Zhong, Chuanrong
    ACS OMEGA, 2022, : 38912 - 38922
  • [38] Synchrosqueezing S-Transform and Its Application in Seismic Spectral Decomposition
    Huang, Zhong-lai
    Zhang, Jianzhong
    Zhao, Tie-hu
    Sun, Yunbao
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (02): : 817 - 825
  • [39] Sparse generalized S-Transform and its application to detection of low-frequency seismic anomalies in reservoirs
    Liu J.
    Cheng X.
    Wu H.
    Zhang J.
    Jiang X.
    Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting, 2023, 58 (03): : 690 - 699
  • [40] Self-Adaptive Generalized S-Transform and Its Application in Seismic Time-Frequency Analysis
    Liu, Naihao
    Gao, Jinghuai
    Zhang, Bo
    Wang, Qian
    Jiang, Xiudi
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (10): : 7849 - 7859