三维多尺度体曲率的算法及应用(英文)
摘要
为了充分提取与挖掘储层结构及构造信息在时间(深度)和空间上的多尺度特征,构造了一种新的三维多尺度体曲率分析方法,并给出了三维体曲率快速提取算法。与常规的体曲率方法相比,本文方法的改进主要体现在以下两个方面:①在体曲率分析中引入时频域分频展开和对应的空间波数域多尺度自适应微分算子,可同时在时间和空间上反映地震信息的多尺度特征;②将不同尺度的体曲率数据进行有机融合,充分利用了不同尺度曲率异常信息,同时突出有效异常,降低噪声影响,为体曲率属性解释奠定基础。利用该方法处理了陆上和海上三维地震资料,实现了对储层展布、断层及裂缝发育带的检测及多尺度特征的有效刻画。
To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature (MSVC) methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties.
To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature (MSVC) methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties.
引文
Al-Dossary, S., and Marfurt, K. J., 2006, 3D volumetric multispectral estimates of reflector curvature and rotation: Geophysics, 71(5), P41 – P51.
Barnes, A. E., 1996, Theory of 2-D complex seismic traceanalysis: Geophysics, 61(1), 264 – 272.
Bergbauer, S., Mukerji, T., and Hennings, P., 2003, Improving curvature analyses of deformed horizons using scale-dependent filtering techniques: AAPG Bulletin, 87(8), 1255 – 1272.
Blumentritt, C. H., Marfurt, K. J., and Sullivan, E. C., 2006Volume-based curvature computations illuminate fracturorientations - Early to mid-Paleozoic, Central BasiPlatform, west Texas: Geophysics, 71(5), P41 – P51.
Buck, D. M., Alam, A., and Taylor, J. D., 2007, Fractured reservoir prediction from 3D seismic volumetric curvature and low frequency imaging: 77th Annual Meeting, Society of Exploration Geophysicists, Expanded Abstracts, 422 – 426.
Chopra, S., and Marfurt, K. J., 2007, Curvature attribute applications to 3D surface seismic data: The Leading Edge, 26(4), 404 – 414.
Chopra, S., and Marfurt, K. J., 2007, Volumetric curvature attributes add value to 3D seismic data interpretation: The Leading Edge, 26(7), 856 – 867.
Chopra, S., and Marfurt, K. J., 2007, Seismic attributes for prospect identification and reservoir characterization: Society of Exploration Geophysicists, Tulsa, USA.
Chopra, S., and Marfurt, K. J., 2008, Emerging and future trends in seismic attributes: The Leading Edge, 27(3), 298 – 318.
Claerbout, J. F., 1976, Fundamentals of Geophysical Data Processing: McGraw-Hill Book Company, New York,USA.
Flierman, W., Weide, J. G., Wever, A., Brouwer, F., and Huck, A., 2008, Use of spatial, frequency and curvature attributes for reservoir, ? uid and contact predictions: 78th Annual Meeting, Society of Exploration Geophysicists, Expanded Abstracts, 1521 – 1525.
Gersztenkorn, A., and Marfurt, K. J., 1999, Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping: Geophysics, 64(5), 1468 – 1479.
Hart, B. S., 2002, Validating seismic attributes: Beyond statistics: The Leading Edge, 21(10), 1016 – 1021.
He, Z. H., Huang, H. D., Hu, G. M., Wang, C. X., and Huang, D. J., 1999, Lateral identification by seismic multi-scale edge detection: Computing Techniques for Geophysical and Geochemical Exploration, 21(4), 289 – 294.
Klein, P., Richard, L., and James, H., 2008, 3D curvature attributes: a new approach for seismic interpretation: First Break, 26, 105 – 112.
Lisle, R. J., 1994, Detection of zones of abnormal strains in structures using Gaussian curvature analysis: AAPG Bulletin, 78(12), 1811 – 1819.
Luo, Y., Higgs, W. G., and Kowalik, W. S., 1996, Edge detection and stratigraphic analysis using 3D seismic data: 66th SEG Annual International Meeting, Society of Exploration Geophysicists, Expanded Abstracts, 324 – 327.
Mallat, S., 2002, A Wavelet tour of signal processing (Second Edition): China Machine Press, Beijing, China.
Marfurt, K. J., 2006, Robust estimates of 3D re? ector dip and azimuth: Geophysics, 71(4), P29 – P40.
Marfurt, K. J., Kirlin, R. L., and Farmer, S. L., 1998, 3-D seismic attributes using a semblance-based coherency algorithm: Geophysics, 63(4), 1150 – 1165.
Marfurt, K. J., Sudhaker, V., Gersztenkorn, A., Crawford, K. D., and Nissen, S. E., 1999, Coherency calculations in the presence of structural dip: Geophysics, 64(1), 104 – 111.
Roberts, A., 2001, Curvature attributes and their application to 3D interpreted horizons: First Break, 19(2), 85 – 100.
Wang, X. W., Yang, K. Q., Zhou, L. H., Wang, J., Liu, H., and Li, Y. M., 2002, Methods of calculating coherence cube on the basis of wavelet transform: Chinese Journal of Geophysics, 45(6), 847 – 852.
Barnes, A. E., 1996, Theory of 2-D complex seismic traceanalysis: Geophysics, 61(1), 264 – 272.
Bergbauer, S., Mukerji, T., and Hennings, P., 2003, Improving curvature analyses of deformed horizons using scale-dependent filtering techniques: AAPG Bulletin, 87(8), 1255 – 1272.
Blumentritt, C. H., Marfurt, K. J., and Sullivan, E. C., 2006Volume-based curvature computations illuminate fracturorientations - Early to mid-Paleozoic, Central BasiPlatform, west Texas: Geophysics, 71(5), P41 – P51.
Buck, D. M., Alam, A., and Taylor, J. D., 2007, Fractured reservoir prediction from 3D seismic volumetric curvature and low frequency imaging: 77th Annual Meeting, Society of Exploration Geophysicists, Expanded Abstracts, 422 – 426.
Chopra, S., and Marfurt, K. J., 2007, Curvature attribute applications to 3D surface seismic data: The Leading Edge, 26(4), 404 – 414.
Chopra, S., and Marfurt, K. J., 2007, Volumetric curvature attributes add value to 3D seismic data interpretation: The Leading Edge, 26(7), 856 – 867.
Chopra, S., and Marfurt, K. J., 2007, Seismic attributes for prospect identification and reservoir characterization: Society of Exploration Geophysicists, Tulsa, USA.
Chopra, S., and Marfurt, K. J., 2008, Emerging and future trends in seismic attributes: The Leading Edge, 27(3), 298 – 318.
Claerbout, J. F., 1976, Fundamentals of Geophysical Data Processing: McGraw-Hill Book Company, New York,USA.
Flierman, W., Weide, J. G., Wever, A., Brouwer, F., and Huck, A., 2008, Use of spatial, frequency and curvature attributes for reservoir, ? uid and contact predictions: 78th Annual Meeting, Society of Exploration Geophysicists, Expanded Abstracts, 1521 – 1525.
Gersztenkorn, A., and Marfurt, K. J., 1999, Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping: Geophysics, 64(5), 1468 – 1479.
Hart, B. S., 2002, Validating seismic attributes: Beyond statistics: The Leading Edge, 21(10), 1016 – 1021.
He, Z. H., Huang, H. D., Hu, G. M., Wang, C. X., and Huang, D. J., 1999, Lateral identification by seismic multi-scale edge detection: Computing Techniques for Geophysical and Geochemical Exploration, 21(4), 289 – 294.
Klein, P., Richard, L., and James, H., 2008, 3D curvature attributes: a new approach for seismic interpretation: First Break, 26, 105 – 112.
Lisle, R. J., 1994, Detection of zones of abnormal strains in structures using Gaussian curvature analysis: AAPG Bulletin, 78(12), 1811 – 1819.
Luo, Y., Higgs, W. G., and Kowalik, W. S., 1996, Edge detection and stratigraphic analysis using 3D seismic data: 66th SEG Annual International Meeting, Society of Exploration Geophysicists, Expanded Abstracts, 324 – 327.
Mallat, S., 2002, A Wavelet tour of signal processing (Second Edition): China Machine Press, Beijing, China.
Marfurt, K. J., 2006, Robust estimates of 3D re? ector dip and azimuth: Geophysics, 71(4), P29 – P40.
Marfurt, K. J., Kirlin, R. L., and Farmer, S. L., 1998, 3-D seismic attributes using a semblance-based coherency algorithm: Geophysics, 63(4), 1150 – 1165.
Marfurt, K. J., Sudhaker, V., Gersztenkorn, A., Crawford, K. D., and Nissen, S. E., 1999, Coherency calculations in the presence of structural dip: Geophysics, 64(1), 104 – 111.
Roberts, A., 2001, Curvature attributes and their application to 3D interpreted horizons: First Break, 19(2), 85 – 100.
Wang, X. W., Yang, K. Q., Zhou, L. H., Wang, J., Liu, H., and Li, Y. M., 2002, Methods of calculating coherence cube on the basis of wavelet transform: Chinese Journal of Geophysics, 45(6), 847 – 852.
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