Seismic attributes via robust and high resolution seismic complex trace analysis

• Autorzy: Kazemnia Kakhki Mohsen , Aghazade Kamal , Mansur Webe João , Peters Franciane Conceição

Identyfikatory

DOI

10.1007/s11600-020-00499-w

• Języki publikacji: EN

Abstrakty

EN

Seismic attribute analysis has been a useful tool for interpretation objectives; therefore, high-resolution images of them are of particular concern. The calculation of these attributes by conventional methods is susceptible to noise, and the conventional fltering supposed to lessen the noise causes the loss of the spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time–frequency decomposition which is stabilised for random noise. The procedure initiates by using sparsity-based, adaptive S-transform to regularise abrupt variations in the frequency content of the non-stationary signals. An adaptive flter is then applied to the previously sparsifed time–frequency spectrum. The proposed zero adaptive flter enhances the high-amplitude frequency components while suppressing the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in the estimation of instantaneous attributes through studying synthetic and real data sets. Seismic attributes estimated by the proposed method are superior to the conventional ones, in terms of robustness and high-resolution imaging. The proposed approach has a detailed application in the interpretation and classifcation of geological structures.

Słowa kluczowe

EN

time–frequency decomposition; zero-tracking adaptive filter; robust window Hilbert transform; sparsity-based adaptive S-transform

PL

rozkład czasowo-częstotliwościowy

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2020
• Tom: Vol. 68, no. 6
• Strony: 1689--1701
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Kazemnia Kakhki Mohsen

• Modelling Methods in Engineering and Geophysics Laboratory (LAMEMO), COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-596, Brazil
• Ferdowsi University of Mashhad, Mashhad, Iran

autor

Aghazade Kamal

• Institute of Geophysics, University of Tehran, Tehran, Iran

autor

Mansur Webe João

• Modelling Methods in Engineering and Geophysics Laboratory (LAMEMO), COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-596, Brazil
• Department of Civil Engineering, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

autor

Peters Franciane Conceição

• Modelling Methods in Engineering and Geophysics Laboratory (LAMEMO), COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-596, Brazil
• Department of Civil Engineering, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Bibliografia

• 1. Ali A, Younas M, Ullah M et al (2019) Characterization of secondary reservoir potential via seismic inversion and attribute analysis: a case study. J Pet Sci Eng. https://doi.org/10.1016/j.petrol.2019.03.039
• 2. Andrade MCB, Porsani MJ, Ursin B (2018) Complex autoregressive time-frequency analysis: estimation of time-varying periodic signal components. IEEE Signal Process Mag. https://doi.org/10.1109/MSP.2017.2783942
• 3. Anees A, Shi W, Ashraf U, Xu Q (2019) Channel identification using 3D seismic attributes and well logging in lower Shihezi formation of Hangjinqi area, northern Ordos Basin. J Appl Geophys. https://doi.org/10.1016/j.jappgeo.2019.02.015
• 4. Asjad A, Mohamed D (2015) A new approach for salt dome detection using a 3D multidirectional edge detector. Appl Geophys. https://doi.org/10.1007/s11770-015-0512-2
• 5. Barnes AE (2007) A tutorial on complex seismic trace analysis. Geophys 10(1190/1):2785048
• 6. Bedi J, Toshniwal D (2019) PP–NFR: an improved hybrid learning approach for porosity prediction from seismic attributes using non-linear feature reduction. J Appl Geophys. https://doi.org/10.1016/j.jappgeo.2019.04.015
• 7. Berthelot A, Solberg AHS, Gelius LJ (2013) Texture attributes for detection of salt. J Appl Geophys. https://doi.org/10.1016/j.jappgeo.2012.09.006
• 8. Chopra S, Marfurt KJ (2005) Seismic attributes A historical perspective. Geophys 70(5):3SO
• 9. Cichostępski K, Kwietniak A, Dec J (2019) Verification of bright spots in the presence of thin beds by AVO and spectral analysis in Miocene sediments of Carpathian Foredeep. Acta Geophys. https://doi.org/10.1007/s11600-019-00324-z
• 10. Farfour M, Yoon WJ, Kim J (2015) Seismic attributes and acoustic impedance inversion in interpretation of complex hydrocarbon reservoirs. J Appl Geophys. https://doi.org/10.1016/j.jappgeo.2015.01.008
• 11. Jones DL, Parks TW (1990) A high resolution data-adaptive time-frequency representation. IEEE Trans Acoust 10(1109/29):61539
• 12. Lamoureux MP, Gibson PC, Grossman JP, Margrave GF (2003) A fast, discrete Gabor transform via a partition of unity. J Fourier Anal Appl
• 13. Liu H, Lei X, Mao C, Li S (2014) Improving reservoir thickness prediction using seismic attributes and attributes fusion. Acta Geophys. https://doi.org/10.2478/s11600-013-0174-5
• 14. Liu J, Marfurt KJ (2007) Instantaneous spectral attributes to detect channels. Geophys 10(1190/1):2428268
• 15. Lukai, Zhang , WCK (2013) Robust estimation of instantaneous phase using a time-frequency adaptive filter. Geophys. https://doi.org/10.1190/GEO2011-0435.1
• 16. Luo Y, Al Dossary S, Marhoon M, Alfaraj M (2003) Generalized hilbert transform and its applications in geophysics. Lead Edge 10(1190/1):1564522
• 17. Obiadi II, Okoye FC, Obiadi CM et al (2019) 3-D structural and seismic attribute analysis for field reservoir development and prospect identification in Fabianski Field, onshore Niger delta, Nigeria. J African Earth Sci. https://doi.org/10.1016/j.jafrearsci.2019.103562
• 18. Partyka G, Gridley J, Lopez J (1999) Interpretational applications of spectral decomposition in reservoir characterization. Lead Edge 18(3):353
• 19. Qi P, Wang Y (2020) Seismic time–frequency spectrum analysis based on local polynomial Fourier transform. Acta Geophys. https://doi.org/10.1007/s11600-019-00377-0
• 20. Radad M, Gholami A, Siahkoohi HR (2015) S-transform with maximum energy concentration: application to non-stationary seismic deconvolution. J Appl Geophys. https://doi.org/10.1016/j.jappgeo.2015.04.010
• 21. Sattari H (2017) High-resolution seismic complex trace analysis by adaptive fast sparse S-transform. Geophys. https://doi.org/10.1190/GEO2015-0425.1
• 22. Sattari H, Gholami A, Siahkoohi HR (2013) Seismic data analysis by adaptive sparse time-frequency decomposition. Geophys. https://doi.org/10.1190/geo2012-0550.1
• 23. Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: The S transform. IEEE Trans Signal Process 10(1109/78):492555
• 24. Takam Takougang EM, Bouzidi Y, Ali MY (2019) Characterization of small faults and fractures in a carbonate reservoir using waveform inversion, reverse time migration, and seismic attributes. J Appl Geophys 161:116–123
• 25. Taner MT, Koehler F, Sheriff RE (1979) Complex seismic trace analysis. Geophys 10(1190/1):1440994
• 26. Verma S, Bhattacharya S, Lujan B et al (2018) Delineation of early Jurassic aged sand dunes and paleo-wind direction in southwestern Wyoming using seismic attributes, inversion, and petrophysical modeling. J Nat Gas Sci Eng. https://doi.org/10.1016/j.jngse.2018.09.022
• 27. Yuan S, Su Y, Wang T et al (2019) Geosteering phase attributes: a new detector for the discontinuities of seismic images. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/LGRS.2018.2866419
• 28. Zheng X, Li Y, Li J, Yu X (2007) Reef and shoal reservoir characterization using paleogeomorpology constrained seismic attribute analysis. In: Society of Exploration Geophysicists - 77th SEG International Exposition and Annual Meeting