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Combining the synchrosqueezing generalized S-transform of variational mode decomposition with the Teager-Kaiser energy operator to calculate the attenuation gradient for identifying oil and gas reservoirs

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Because of the limited resolution of conventional time–frequency analysis algorithms, they are also limited to calculate attenuation gradients that describe oil and gas reservoirs. We propose an advanced method for calculating the attenuation gradient that combines the synchrosqueezing generalized S-transform of variational mode decomposition with the Teager–Kaiser energy operator. SSVGST takes advantage of the synchrosqueezing generalized S-transform to focus the longitudinal resolution of the time–frequency domain and variational mode decomposition for adaptive signal segmentation in the frequency domain. Thus, SSVGST can be used to improve the time–frequency resolution of seismic signals, and the Teager–Kaiser energy operator is used to enhance the extracted attenuation gradient and highlight oil and gas regions effectively. The time–frequency focusing ability of SSVGST was verified by using a synthetic signal and theoretical model. Experimental results with the model and field data showed that the combination of SSVGST with the Teager–Kaiser energy operator suppressed the fuzzy energy caused by the low resolution of conventional time–frequency analysis algorithms and could locate reservoirs accurately and effectively.
Czasopismo
Rocznik
Strony
795--812
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, Sichuan, China
autor
  • State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Key Lab of Earth Exploration and Information Techniques of Ministry of Education, College of Geophysics, Chengdu University of Technology, Chengdu 610059, Sichuan, China
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, Sichuan, China
  • State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Key Lab of Earth Exploration and Information Techniques of Ministry of Education, College of Geophysics, Chengdu University of Technology, Chengdu 610059, Sichuan, China
  • School of Education, China West Normal University, Nanchong 637002, China
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, Sichuan, China
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, Sichuan, China
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, Sichuan, China
Bibliografia
  • 1. Chen X-H, He Z-H, Huang D-J, Wen X-T (2009) Low frequency shadow detection of gas reservoirs in time-frequency domain. Chin J Geophys 52(1):215–221 (in Chinese)
  • 2. Daubechies I, Lu J, Wu HT (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30:243–261
  • 3. Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544
  • 4. Huang ZL, Zhang J-Z (2016) Synchrosqueezing S transform. SCIENTIA SINICA Inf 46(05):643–650
  • 5. Jiang X, Cao J, Yang J et al (2020) AVO analysis combined with teager-kaiser energy methods for hydrocarbon detection. IEEE Geosci Remote Sens Lett 19:1–5
  • 6. Mitchell JT, Derzhi N, Lichman E et al (1996) Energy absorption analysis: a case study. In: Paper presented at the 1996 SEG Annual Meeting, Denver, Colorado, November, vol 15, p 1785–1788
  • 7. Pham DH, Meignen S (2017) High-order synchrosqueezing transform for multicomponent signals analysis-With an application to gravitational-wave signal. IEEE Trans Signal Process 65(12):3168–3178
  • 8. Pinnegar CR, Mansinha L (2003) The S-transform with windows of arbitrary and varying shape. Geophysics 68(1):381–385
  • 9. Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44(4):998–1001
  • 10. Wang Q, Gao J, Liu N et al (2018) High-resolution seismic time–frequency analysis using the synchrosqueezing generalized S-transform. IEEE Geosci Remote Sens Lett 15(3):374–378
  • 11. Xiong X, He Z (2011) High-precision frequency attenuation analysis and its application. Appl Geophys 8(4):337–343
  • 12. Xue Y-J, Cao J-X, Tian R-F (2014a) EMD and Teager–Kaiser energy applied to hydrocarbon detection in a carbonate reservoir. Geophys J Int 197(1):277–291
  • 13. Xue Y-J, Cao J-X, Tian R-F, Du H-K, Shu Y-X (2014b) Application of the empirical mode decomposition and wavelet transform to seismic reflection frequency attenuation analysis. J Petrol Sci Eng 122:360–370
  • 14. Xue Y-J et al (2016) Application of the variational-mode decomposition for seismic time–frequency analysis. IEEE J Sel Top Appl Earth Obs Remote Sens 9(8):3821–3831
  • 15. Yu G, Zhou Y (2016) General linear chirplet transform. Mech Syst Signal Process 70:958–973
  • 16. Zhong-Lai H, Jian-Zhong Z, Zhi-Hui Z (2017) A second-order synchrosqueezing S-transform and its application in seismic spectral decomposition. Chin J Geophys 60(7):2833–2844. https://doi.org/10.6038/cjg20170728
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2bc82a6f-bd33-45c2-8e97-041e51631a60
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