基于地震纹理属性和模糊聚类划分地震相
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摘要
基于地震纹理属性和模糊聚类划分地震相技术是一种属性综合聚类方法,因此选取何种参数(即输入属性体的类型、聚类或分类方法、地震相的地质意义解释等因素)描述地震相的地震反射特征是决定该类方法特点和应用效果的主要因素。本文联合应用模糊C均值聚类与地震纹理属性实现地震相的自动划分,针对三维地震数据体,该法为每个数据点在其三维邻域空间内从不同方向提取多种代表该点的地震纹理属性的特征参数,然后利用模糊C均值聚类方法对获取的特征参数集进行自动划分,从而确定每个数据点所属的相带。实际应用结果表明,该法的地震划相效果理想,与钻井结果基本吻合。
Seismic facies classification technology based on seismic texture attributes and fuzzy clustering is an attributes clustering method.Therefore,parameters to be selected(the type of input attributes,the method of clustering or classification,the interpretation of seismic facies geological significance,and so on)to describe the reflection characteristics of seismic facies are main factors to determine technical characteristics and application results of the methods.The paper combines the fuzzy C-means clustering algorithm and the seismic texture attribute to achieve the automatic seismic facies classification.For each point in 3Dseismic data volume,we extract seismic textual features from different directions in its neighborhood,and then divide seismic facies using the fuzzy C-means clustering method based on the attributes,so as to determine the facies which each point belongs to.The practical application results show that the algorithm is effective,and consistent well with the drilling results.
Seismic facies classification technology based on seismic texture attributes and fuzzy clustering is an attributes clustering method.Therefore,parameters to be selected(the type of input attributes,the method of clustering or classification,the interpretation of seismic facies geological significance,and so on)to describe the reflection characteristics of seismic facies are main factors to determine technical characteristics and application results of the methods.The paper combines the fuzzy C-means clustering algorithm and the seismic texture attribute to achieve the automatic seismic facies classification.For each point in 3Dseismic data volume,we extract seismic textual features from different directions in its neighborhood,and then divide seismic facies using the fuzzy C-means clustering method based on the attributes,so as to determine the facies which each point belongs to.The practical application results show that the algorithm is effective,and consistent well with the drilling results.
引文
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[16]Haraljck Robert M,Shanmugam K.Textural features forimage classification.IEEE Transacations on System,Man and Cybernetics,1973,3(6):610～621
[17]Chopra Satinder,Marfurt K J.Emerging and futuretrends in seismic attributes.The Leading Edge,2008,27(3):298～318
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[19]Chopra Satinder,Alexeev Vladimir.Applications oftexture attribute analysis to 3Dseismic data.The Lead-ing Edge,2006,25(8):934～940
[20]Yenugu Malleswar,Marfurt K J.Seismic textureanalysis for reservoir prediction and characterization.The Leading Edge,2010,29(9):1116～1121
[21]王治国,尹成等.河道纹理属性分析中的灰度共生矩阵参数研究.石油地球物理勘探,2012,47(1):100～106Wang Zhiguo,Yin Cheng et al.GLCM parameters influvial texture analysis.OGP,2012,47(1):100～106
[22]王伟.地震几何属性识别断层技术研究及应用[硕士学位论文].山东东营:中国石油大学(华东),2009
[23]崔世凌,张军华,王伟等.地震纹理属性在JJD工区断层识别中的应用.物探化探计算技术,2010,32(3):304～309Cui Shiling,Zhang Junhua,Wang Wei et al.The extrac-tion of seismic texture attribute and its application in faultidentification.Computing Techniques for Geophysicaland Geochemical Exploration,2010,32(3):304～309
[24]Dunn J C.A fuzzy relative of the ISODATA process andits use in detecting compact well-separated clusters.Journal of Cybernetics,1974,3(3):32～57
[25]Bezdek J C.Pattern Recognition with Fuzzy ObjectiveFunction Algorithms.New York:Plenum Press,1981
[2]Neves F A,Trievwasser H.Multi-attribute seismicvolume facies classification for predicting fractures in car-bonate reservoirs.The Leading Edge,2006,25(6):698～700
[3]West B P,May S R,Eastwood J E et al.Interactive seis-mic facies classification using textural attributes and neu-ral networks.The Leading Edge,2002,21(10):1042～1049
[4]Gao Dengliang.Volume texture extraction for 3Dseismic visualization and interpretation.Geophysics,2003,68(4):1294～1302
[5]Gao Dengliang.Application of three-dimensional seismictexture analysis with special reference to deep-marine fa-cies discrimination and interpretation:Offshore Angola,west Africa.AAPG Bulletin,2007,91(12):1665～1683
[6]Angelo S M,Matos M and Marfurt K J.Integrated seis-mic texture segmentation and clustering analysis to im-proved delineation of reservoir geometry.SEG TechnicalProgram Expanded Abstracts,2009,28:1107～1111
[7]Matos M,Angelo S M and Marfurt K J.Integrated seis-mic texture segmentation and cluster analysis applied tochannel delineation and chert reservoir characterization.Geophysics,2011,76(5):P11～P21
[8]杨培杰,印兴耀,张广智.模糊C均值地震属性聚类分析.石油地球物理勘探,2007,42(3):322～324,347Yang Peijie,Yin Xingyao,Zhang Guangzhi.Clusterananlysis of seismic attributes by fuzzy C-mean algo-rithm.OGP,2007,42(3):322～324,347
[9]Chuang K S,Tzeng H L et al.Fuccy c-means clusteringwith spatial information for image segmentation.Comput-erized Medical Imaging and Graphics,2006,30(1):9～15
[10]Tamalika Chaira.A novel intuitionistic fuzzy C meansclustering algorithm and its application to medical ima-ges.Applied Soft Computing,2011,11(2):1711～1717
[11]Ahmed M N,Yamany S M et al.A modified fuzzyc-means algorithm for bias field estimation and segmenta-tion of MRI data.IEEE Transactions on Medical Ima-ging,2002,21(3):193～199
[12]Carvalho Francisco.Fuzzy c-means clustering methods forsymbolic interval data.Pattern Recognition Letters,2007,28(4):423～437
[13]Kannan S R,Ramathilagam S,Chung P C.Effective fuzzyc-means clustering algorithm for data clustering.ExpertSystems with Application,2012,39(7):6292～6300
[14]Fan Jianchao,Han Min,Wang Jun.Single point itenativeweighted fuzzy c-means clustering algorithm for remotesensing image segmentation.Pattern Recognation,2009,42(11):2527～2540
[15]Thitimajshima P.A new modified fuzzy c-means algo-rithm for multispectral satellite images segmentation.IGARSS,2000,4:1684～1686
[16]Haraljck Robert M,Shanmugam K.Textural features forimage classification.IEEE Transacations on System,Man and Cybernetics,1973,3(6):610～621
[17]Chopra Satinder,Marfurt K J.Emerging and futuretrends in seismic attributes.The Leading Edge,2008,27(3):298～318
[18]Chopra Satinder,Alexeev Vladimir.Applications oftexture attribute analysis to 3Dseismic data.SEG Tech-nical Program Expanded Abstracts,2005,24:767～770
[19]Chopra Satinder,Alexeev Vladimir.Applications oftexture attribute analysis to 3Dseismic data.The Lead-ing Edge,2006,25(8):934～940
[20]Yenugu Malleswar,Marfurt K J.Seismic textureanalysis for reservoir prediction and characterization.The Leading Edge,2010,29(9):1116～1121
[21]王治国,尹成等.河道纹理属性分析中的灰度共生矩阵参数研究.石油地球物理勘探,2012,47(1):100～106Wang Zhiguo,Yin Cheng et al.GLCM parameters influvial texture analysis.OGP,2012,47(1):100～106
[22]王伟.地震几何属性识别断层技术研究及应用[硕士学位论文].山东东营:中国石油大学(华东),2009
[23]崔世凌,张军华,王伟等.地震纹理属性在JJD工区断层识别中的应用.物探化探计算技术,2010,32(3):304～309Cui Shiling,Zhang Junhua,Wang Wei et al.The extrac-tion of seismic texture attribute and its application in faultidentification.Computing Techniques for Geophysicaland Geochemical Exploration,2010,32(3):304～309
[24]Dunn J C.A fuzzy relative of the ISODATA process andits use in detecting compact well-separated clusters.Journal of Cybernetics,1974,3(3):32～57
[25]Bezdek J C.Pattern Recognition with Fuzzy ObjectiveFunction Algorithms.New York:Plenum Press,1981
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