特征值相干理论诠释及效果比较
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摘要
相干体技术是目前应用最为广泛的地震解释技术之一,已由第一代互相关算法(C1)、第二代多道相似算法(C2)发展到第三代多道特征值相干算法(C3).常规的基于特征值的第三代相干(C31)定义为第一特征值与所有特征值之和的比.当采用最主要的3道算法时,也可以用Randen等(C32)、Bakker(C33)、Donias等(C34)、Wu(C35)给出的相干公式进行计算.本文比较了这5种表征方法,模型测试和实际应用结果表明:(1)C31、C34、C35都可以较好地表征相干,当储层较平时C35有不错的抗噪性,对于复杂断块储层,C31即俗称的第三代相干应是首选;(2)储层简单时C32特征值计算结果为负值,储层复杂时其特征值有正值也有负值,物理含义不明确,应用效果也不好;(3)C33计算的是不相干属性,抗噪性一般.在特征值相干的计算过程中,数据道计算窗长度的选取很重要,对于精细勘探而言,拟根据目标体的大小,在1/2波长到3/2波长时窗范围内,选取不同大小时窗,进行分级对比研究.本文的研究成果对于广大解释人员如何用好相干体这一实用技术,有一定的指导意义和借鉴作用.
Coherence is one of the most widely used seismic interpretation techniques, which has been developed from the first generation cross correlation algorithm(C1), the second generation multi-channel semblance-based algorithm(C2) to the third generation multi-channel eigenvalue coherence algorithm(C3). The conventional third generation coherence(C31) based on eigenvalues is defined as the ratio of the largest eigenvalue to the sum of all eigenvalues. When three traces algorithm is adopted, the formulas given by Randen et al.(C32), Bakker(C33), Donias et al.(C34) and Wu(C35) can also be used for representing the coherence. In this paper, the five characterization methods are used and compared, and the results of model test and practical application show that:(1) C31, C34 and C35 can better characterize coherence. C35 can denoise better when the reservoir is relatively flat and C31 commonly known as the third-generation coherence should be the first choice for complex fault block reservoirs.(2) The calculating eigenvalues of C32 are all negative when the reservoir is simple, and positive or negative when the reservoir is complex. The physical meaning of C32 is not clear and the application effect is not good.(3) The calculated result of C33 is non-coherence attribute, and its anti-noise ability is not so good. For eigenvalue coherence, it is of great importance to select the length of calculation window. For fine seismic exploration, time windows of different size should be selected in the window range from 1/2 wavelength to 3/2 wavelength according to the size of the target body to conduct a graded comparative study. The research results of this paper are of the general guiding significance and referential effect for explainers how to make good use of coherence technique.
Coherence is one of the most widely used seismic interpretation techniques, which has been developed from the first generation cross correlation algorithm(C1), the second generation multi-channel semblance-based algorithm(C2) to the third generation multi-channel eigenvalue coherence algorithm(C3). The conventional third generation coherence(C31) based on eigenvalues is defined as the ratio of the largest eigenvalue to the sum of all eigenvalues. When three traces algorithm is adopted, the formulas given by Randen et al.(C32), Bakker(C33), Donias et al.(C34) and Wu(C35) can also be used for representing the coherence. In this paper, the five characterization methods are used and compared, and the results of model test and practical application show that:(1) C31, C34 and C35 can better characterize coherence. C35 can denoise better when the reservoir is relatively flat and C31 commonly known as the third-generation coherence should be the first choice for complex fault block reservoirs.(2) The calculating eigenvalues of C32 are all negative when the reservoir is simple, and positive or negative when the reservoir is complex. The physical meaning of C32 is not clear and the application effect is not good.(3) The calculated result of C33 is non-coherence attribute, and its anti-noise ability is not so good. For eigenvalue coherence, it is of great importance to select the length of calculation window. For fine seismic exploration, time windows of different size should be selected in the window range from 1/2 wavelength to 3/2 wavelength according to the size of the target body to conduct a graded comparative study. The research results of this paper are of the general guiding significance and referential effect for explainers how to make good use of coherence technique.
引文
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Claerbout J F,Fomel S.2012.Image estimation by example:Geophysical soundings image construction [M].Stanford Exploration Project.
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Gersztenkorn A,Marfurt K J.1999.Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping [J].Geophysics,64(5):1468-1479.
Li T T,Hou S Y,Ma S Z et al.2018.Overview and research progress of fault identification method [J].Progress in Geophysics (in Chinese),33(4):1507-1514,doi:10.6038/pg2018BB0311.
Lin T F,Ha T,Marfurt K J et al.2016.Quantifying the significance of coherence anomalies [J].Interpretation,4(2):T205-T213.
Marfurt K J,Kirlin R L,Farmer S L et al.1998.3-D seismic attributes using a semblance-based coherency algorithm [J].Geophysics,63(4):1150-1165.
Randen T,Monsen E,Signer C et al.2000.Three-dimensional texture attributes for seismic data analysis [C].Expanded Abstracts of 70th Ann.Internat.SEG Mtg,668- 671.
Satinder C,Marfurt K J.2018.Coherence attribute applications on seismic data in various guises [J].Interpretation,6(3):T521-T529.
Wu X M.2017.Directional structure-tensor-based coherence to detect seismic faults and channels [J].Geophysics,82(2):A13-A17.
Zhang J H.2012.Fine seismic description technology of fault block and fractured reservoir [M].Dongying:China University of Petroleum Press.
李婷婷,侯思宇,马世忠,等.2018.断层识别方法综述及研究进展[J].地球物理学进展,33(4):1507-1514,doi:10.6038/pg2018BB0311.
张军华.2012.断块、裂缝型油气藏地震精细描述技术[M].东营:中国石油大学出版社.
Bakker P.2002.Image structure analysis for seismic interpretation [Ph.D.thesis].Technical University Delft,41-80.
Claerbout J F,Fomel S.2012.Image estimation by example:Geophysical soundings image construction [M].Stanford Exploration Project.
Donias M,David C,Berthoumieu Y et al.2007.New fault attribute based on robust directional scheme [J].Geophysics,72(4):P39-P46.
Gersztenkorn A,Marfurt K J.1999.Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping [J].Geophysics,64(5):1468-1479.
Li T T,Hou S Y,Ma S Z et al.2018.Overview and research progress of fault identification method [J].Progress in Geophysics (in Chinese),33(4):1507-1514,doi:10.6038/pg2018BB0311.
Lin T F,Ha T,Marfurt K J et al.2016.Quantifying the significance of coherence anomalies [J].Interpretation,4(2):T205-T213.
Marfurt K J,Kirlin R L,Farmer S L et al.1998.3-D seismic attributes using a semblance-based coherency algorithm [J].Geophysics,63(4):1150-1165.
Randen T,Monsen E,Signer C et al.2000.Three-dimensional texture attributes for seismic data analysis [C].Expanded Abstracts of 70th Ann.Internat.SEG Mtg,668- 671.
Satinder C,Marfurt K J.2018.Coherence attribute applications on seismic data in various guises [J].Interpretation,6(3):T521-T529.
Wu X M.2017.Directional structure-tensor-based coherence to detect seismic faults and channels [J].Geophysics,82(2):A13-A17.
Zhang J H.2012.Fine seismic description technology of fault block and fractured reservoir [M].Dongying:China University of Petroleum Press.
李婷婷,侯思宇,马世忠,等.2018.断层识别方法综述及研究进展[J].地球物理学进展,33(4):1507-1514,doi:10.6038/pg2018BB0311.
张军华.2012.断块、裂缝型油气藏地震精细描述技术[M].东营:中国石油大学出版社.
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