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Characterization of fractured zones via topological analysis of 3D seismic diffraction images. / Protasov, Maxim I.; Khachkova, Tatyana S.; Kolyukhin, Dmitriy R. и др.

в: Geophysics, Том 84, № 5, 01.09.2019, стр. O93-O102.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

Harvard

Protasov, MI, Khachkova, TS, Kolyukhin, DR & Bazaikin, YV 2019, 'Characterization of fractured zones via topological analysis of 3D seismic diffraction images', Geophysics, Том. 84, № 5, стр. O93-O102. https://doi.org/10.1190/GEO2018-0431.1

APA

Vancouver

Protasov MI, Khachkova TS, Kolyukhin DR, Bazaikin YV. Characterization of fractured zones via topological analysis of 3D seismic diffraction images. Geophysics. 2019 сент. 1;84(5):O93-O102. doi: 10.1190/GEO2018-0431.1

Author

Protasov, Maxim I. ; Khachkova, Tatyana S. ; Kolyukhin, Dmitriy R. и др. / Characterization of fractured zones via topological analysis of 3D seismic diffraction images. в: Geophysics. 2019 ; Том 84, № 5. стр. O93-O102.

BibTeX

@article{92db0eb757364924af8c898fe299d970,
title = "Characterization of fractured zones via topological analysis of 3D seismic diffraction images",
abstract = "A workflow for recovering fracture network characteristics from seismic data is considered. First, the presented discrete fracture modeling technique properly describes fracture models on the seismic scale. The key procedure of the workflow is 3D diffraction imaging based on the spectral decomposition of different combinations of selective images. Selective images are obtained by the prestack asymmetric migration procedure, whereas spectral decomposition occurs in the Fourier domain with respect to the spatial dip and the azimuth angles. At the final stage, we performed a topological analysis based on the construction of a merge tree from the obtained diffraction images. The results of the topological algorithm are modeling parameters for the discrete fractures. To analyze the effectiveness of our workflow, a statistical comparison of the recovered parameters and true model parameters was conducted. We used the Kolmogorov-Smirnov test for the statistical analysis of the fracture lengths, whereas the behavior of the Morisita index indicates the statistical distribution of the modeled fracture corridors. Numerical examples with synthetic realistic models provide a detailed, reliable reconstruction of the statistical characteristics of the fracture corridors.",
keywords = "3D diffraction images, computational and applied topology, discrete fracture networks, fractured zones, merge tree",
author = "Protasov, {Maxim I.} and Khachkova, {Tatyana S.} and Kolyukhin, {Dmitriy R.} and Bazaikin, {Yaroslav V.}",
year = "2019",
month = sep,
day = "1",
doi = "10.1190/GEO2018-0431.1",
language = "English",
volume = "84",
pages = "O93--O102",
journal = "Geophysics",
issn = "0016-8033",
publisher = "SOC EXPLORATION GEOPHYSICISTS",
number = "5",

}

RIS

TY - JOUR

T1 - Characterization of fractured zones via topological analysis of 3D seismic diffraction images

AU - Protasov, Maxim I.

AU - Khachkova, Tatyana S.

AU - Kolyukhin, Dmitriy R.

AU - Bazaikin, Yaroslav V.

PY - 2019/9/1

Y1 - 2019/9/1

N2 - A workflow for recovering fracture network characteristics from seismic data is considered. First, the presented discrete fracture modeling technique properly describes fracture models on the seismic scale. The key procedure of the workflow is 3D diffraction imaging based on the spectral decomposition of different combinations of selective images. Selective images are obtained by the prestack asymmetric migration procedure, whereas spectral decomposition occurs in the Fourier domain with respect to the spatial dip and the azimuth angles. At the final stage, we performed a topological analysis based on the construction of a merge tree from the obtained diffraction images. The results of the topological algorithm are modeling parameters for the discrete fractures. To analyze the effectiveness of our workflow, a statistical comparison of the recovered parameters and true model parameters was conducted. We used the Kolmogorov-Smirnov test for the statistical analysis of the fracture lengths, whereas the behavior of the Morisita index indicates the statistical distribution of the modeled fracture corridors. Numerical examples with synthetic realistic models provide a detailed, reliable reconstruction of the statistical characteristics of the fracture corridors.

AB - A workflow for recovering fracture network characteristics from seismic data is considered. First, the presented discrete fracture modeling technique properly describes fracture models on the seismic scale. The key procedure of the workflow is 3D diffraction imaging based on the spectral decomposition of different combinations of selective images. Selective images are obtained by the prestack asymmetric migration procedure, whereas spectral decomposition occurs in the Fourier domain with respect to the spatial dip and the azimuth angles. At the final stage, we performed a topological analysis based on the construction of a merge tree from the obtained diffraction images. The results of the topological algorithm are modeling parameters for the discrete fractures. To analyze the effectiveness of our workflow, a statistical comparison of the recovered parameters and true model parameters was conducted. We used the Kolmogorov-Smirnov test for the statistical analysis of the fracture lengths, whereas the behavior of the Morisita index indicates the statistical distribution of the modeled fracture corridors. Numerical examples with synthetic realistic models provide a detailed, reliable reconstruction of the statistical characteristics of the fracture corridors.

KW - 3D diffraction images

KW - computational and applied topology

KW - discrete fracture networks

KW - fractured zones

KW - merge tree

UR - http://www.scopus.com/inward/record.url?scp=85091396351&partnerID=8YFLogxK

U2 - 10.1190/GEO2018-0431.1

DO - 10.1190/GEO2018-0431.1

M3 - Article

VL - 84

SP - O93-O102

JO - Geophysics

JF - Geophysics

SN - 0016-8033

IS - 5

ER -

ID: 23727184