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Tytuł artykułu

Multi sparsity based spectral attributes for discontinuity detection

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Structural and stratigraphic discontinuities, such as faults and channels, generally contribute to the construction of traps and reservoirs. Spectral decomposition can utilize the sensitivities of different frequency components to different geological conditions to identify these geological anomalies. The sparse inverse spectral decomposition (SISD) involves a sparse constraint of time–frequency spectra, and one critical parameter is the sparsity which determines the time–frequency resolution. A small sparsity gives a low temporal resolution result that cannot be used for thin-bed detection. Conversely, a large sparsity provides a high-resolution result, but it may lose weak refection signals. The complex geological conditions in the subsurface will lead to some difficulties in detecting the discontinuities by using the SISD method with a fixed sparsity. To address this issue, we propose multi-sparsity-based spectral attributes by fusing the amplitude spectra results of three different sparsities to detect subsurface discontinuities. Compared with the fixed sparsity, the multi-sparsity-based spectral attributes can detect more geological details and highlight geological edges more clearly. The application on a 3D real data with an area of 230 km2 from deep formation in Northwest China exhibits its effectiveness in discontinuity detection. The proposed method can detect the weak or small hidden geological details more and better than the fixed sparsity method, suggesting that it may serve as a future tool for detecting the distribution of geological abnormalities in subsurface.
Czasopismo
Rocznik
Strony
1059--1069
Opis fizyczny
Bibliogr. 41 poz.
Twórcy
autor
  • State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum, Changping, Beijing 102249, China
autor
  • State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum, Changping, Beijing 102249, China
autor
  • State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum, Changping, Beijing 102249, China
autor
  • Northwest Branch of Research Institute of Petroleum Exploration and Development, Petrochina, Lanzhou, China
autor
  • State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum, Changping, Beijing 102249, China
Bibliografia
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  • 14. Huang W, Zhang S, Zhang CC, Wei W (2013) Sequence configuration and sedimentary evolution of nenjiang formation in the songliao basin. Acta Sedimentol Sin 31:920–927
  • 15. Li Q, Di BR, Wei JX, Yuan SY, Shi WP (2016) The identification of multi-cave combinations in carbonate reservoirs based on sparsity constraint inverse spectral decomposition. J Geophys Eng 13:940–952
  • 16. Li FY, Qi J, Lyu B, Marfurt KJ (2018) Multispectral coherence. Interpretation 6(1):T61–T69
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  • 20. Luo C, Li XY, Huang GT (2018) Hydrocarbon identification by application of improved sparse constrained inverse spectral decomposition to frequency-dependent AVO inversion. J Geophys Eng 15:1446–1459
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  • 22. Mittempergher S, Pennacchioni G, Di Toro G (2009) The effects of fault orientation and fluid infiltration on fault rock assemblages at seismogenic depths. J Struct Geol 31(12):1511–1524
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  • 32. Wang ZW, Wang XL, Min X et al (2012) Reservoir information extraction using a fractional fourier transform and a smooth pseudo Wigner–Ville distribution. Appl Geophys 9:391–400
  • 33. Wang X, Zhang B, Li F, Qi J, Bai B (2016) Seismic time-frequency decomposition by using a hybrid basis-matching pursuit technique. Interpretation 4(2):T263–T272
  • 34. Wang Q, Gao JH, Liu NH, Jiang XD (2018a) High-Resolution seismic time–frequency analysis using the synchrosqueezing generalized S-transform. IEEE Geosci Remote Sens Lett 15(3):374–378
  • 35. Wang SX, Yuan SY, Wang TY, Gao JH, Li SJ (2018b) Three-dimensional geosteering coherence attributes for deep-formation discontinuity detection. Geophysics 82(6):O83–O89
  • 36. Wang TY, Yuan SY, Song ZH et al (2018c) Application of sparse inverse spectral attributes to channels detection. In: 80th annual international meeting, EAGE, Expanded Abstracts
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  • 38. Yuan SY, Wang SX, Ma M, Ji YZ, Deng L (2017) Sparse Bayesian learning-based time-variant deconvolution. IEEE Trans Geosci Remote Sens 55(11):6182–6194
  • 39. Yuan SY, Su YX, Wang TY, Wang JH, Wang SX (2019a) Geosteering phase attributes: a new detector for the discontinuities of seismic images. IEEE Geosci Remote Sens Lett 16(1):145–149
  • 40. Yuan SY, Ji YZ, Shi PD, Zeng J, Gao JH, Wang SX (2019b) Sparse Bayesian learning-based seismic high-resolution time-frequency analysis. IEEE Geosci Remote Sens Lett 16(4):623–627
  • 41. Yuan SY, Wang SX, Luo YN, Wei WW, Wang GC (2019c) Impedance inversion by using the low-frequency full-waveform inversion result as a priori model. Geophysics 84(2):R149–R164
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9f92f163-be0c-4655-a873-0b696a91e79f
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