基于Matlab的地层多尺度定量分析方法研究
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摘要
本文以Matlab为平台,以层序地层学理论作为指导,运用小波变换、主成分分析、聚类分析等方法对测井数据进行处理,充分挖掘测井资料中所蕴含的地质信息,借以研究层序单元的小波分析模式以及岩石地层单元的多元统计模式。在研究中,借助于Matlab进行编程,成功的实现了对测井数据的插值、主成份分析、小波分析、聚类分析等功能;运用所编程序的插值模块对典型测井曲线进行插值,对采用不同插值参数的差值结果进行了对比,选择linear作为插值的参数;利用程序中的小波变换功能,对典型测井曲线进行分析,研究表明,当最大值取数据个数的1/8、尺度a为81时对层序单元的划分有利,当最大值取数据个数的1/10,尺度a为21时对准层序单元的划分有利;利用程序中的主成份分析模块对测井数据进行了主成份分析,提高了第一主成分作为小波变换对象的可靠性,利用典型测井曲线的分析结果,对济阳-临清地区的7口井进行了层序和准层序的划分;在岩石地层单元的多元统计模式的研究中,主要运用的是程序中的聚类分析,其中包括层次聚类和k均值聚类,运用此功能本文对济阳—临清地区6口井的山西组和太原组岩性进行聚类分析,从中得出了岩石地层单元的多元统计模式,即山西组在层次聚类聚6类时的类间距离值一般在5-7之间,而太原组的则在3-7之间,进行k均值聚类时,山西组在k=4时其轮廓平均值不是所有类的轮廓平均值中的最小值,k=3时其对应的平均值不是所有类的轮廓平均值中的最大值,而太原组则是在k=3时轮廓平均值不是所有类的轮廓平均值中的最小值,并且此模式在DG2的分析中得到了验证,取得了较好的效果。
Regarded Matlab as a platform and Guided by sequence theories, this article adopts a series of methods such as wavelet transform, principal component analysis, clustering analysis and so on to deal with well-logging data and fully dig geological information which implies in well-logging information to study wavelet analysis pattern of sequence unit and multivariate statistical pattern of rock stratigraphic unit. In this article, Matlab is used to compile programme. By doing this some functions such as interpolation, principal component analysis, wavelet transform and clustering analysis are successfully realized. We use interpolation module acquired by compiling pogramme to interpolate typical well-logging curves. By comparing the interpolating results that adopt different interpolation parameters we consider linear as interpolation parameter. By making use of wavelet transform function in the compiled programme to analyze typical well-logging curves it shows that it is favorable to divide sequence units when dimension is 81 and the maximum value is one-eighth of well-logging data number; It is favorable to divide subquence units when dimension is 21 and the maximum is one-tenth of well-logging data number. After using the PCA module in the programme to analyze well-logging data, the validity of regarding the first principal component as the object of wavelet transform can be proved. Combined with the analysis results of typical well-logging curves, seven wells in Jiyang-Linqing area are used to divide sequences and subsequences. In researching multivariate statistical pattern of rock stratigraphic units it mainly adopts the clustering analysis module, which includes hierachical clustering analysis module and K-means clustering analysis module. By dealing lithology of Shanxi and Taiyuan Formation of seven wells in Jiyang-Linqing area with clustering analysis we can get some multivariate statistical patterns of rock stratigraphic units.That is to say, for Shanxi Formation when cluster number is 6 the values of hierarchical clustering intercluster distance are between 5 and 7 while those of Taiyuan are between 3 and 7. After doing K-means clustering analysis it shows that for Shanxi Formation the silhouette average values are not the minimum among all clusters' silhouette average values when k is 4. And those average values are not the maximum while those of Taiyuan Formation are not the minimum among all clusters' silhouette averager values when k is 3. This kind of pattern is verified in analyzing DG2 and get better effect.
Regarded Matlab as a platform and Guided by sequence theories, this article adopts a series of methods such as wavelet transform, principal component analysis, clustering analysis and so on to deal with well-logging data and fully dig geological information which implies in well-logging information to study wavelet analysis pattern of sequence unit and multivariate statistical pattern of rock stratigraphic unit. In this article, Matlab is used to compile programme. By doing this some functions such as interpolation, principal component analysis, wavelet transform and clustering analysis are successfully realized. We use interpolation module acquired by compiling pogramme to interpolate typical well-logging curves. By comparing the interpolating results that adopt different interpolation parameters we consider linear as interpolation parameter. By making use of wavelet transform function in the compiled programme to analyze typical well-logging curves it shows that it is favorable to divide sequence units when dimension is 81 and the maximum value is one-eighth of well-logging data number; It is favorable to divide subquence units when dimension is 21 and the maximum is one-tenth of well-logging data number. After using the PCA module in the programme to analyze well-logging data, the validity of regarding the first principal component as the object of wavelet transform can be proved. Combined with the analysis results of typical well-logging curves, seven wells in Jiyang-Linqing area are used to divide sequences and subsequences. In researching multivariate statistical pattern of rock stratigraphic units it mainly adopts the clustering analysis module, which includes hierachical clustering analysis module and K-means clustering analysis module. By dealing lithology of Shanxi and Taiyuan Formation of seven wells in Jiyang-Linqing area with clustering analysis we can get some multivariate statistical patterns of rock stratigraphic units.That is to say, for Shanxi Formation when cluster number is 6 the values of hierarchical clustering intercluster distance are between 5 and 7 while those of Taiyuan are between 3 and 7. After doing K-means clustering analysis it shows that for Shanxi Formation the silhouette average values are not the minimum among all clusters' silhouette average values when k is 4. And those average values are not the maximum while those of Taiyuan Formation are not the minimum among all clusters' silhouette averager values when k is 3. This kind of pattern is verified in analyzing DG2 and get better effect.
引文
[1]张威MATLAB应用程序集成与发布[M].西安地质科技大学出版社,2005
[2]龚一鸣等,地层学基础与前沿[M],中国地质大学出版社,2000,12
[3]车卓吾.测井资料分析手册[M].北京:石油工业出版社,1995
[4]向导科技,MATLAB6.0程序设计与实例应用[M],中国铁道出版社,2001,1-17
[5]飞思科技产品研发中心,神经网络理论与MATLAB 7实现[M],电子工业出版社,2005.3
[6]王旭东等,MATLAB及其在FPGA中的应用[M],国防工业出版社,2006
[7](美)Edward B.Magrab,高会生等,MATLAB原理与工程应用[M],电子工业出版社,2006.4--11
[8]苏金明等,MATLAB工程数学[M],电子工业出版社,2005
[9]刘明寿,周晓明,戴国俊,等.用归一化方法评价科技期刊学术质量与常规评定结果的相关性分析[J],编辑学报,2006,18(6):468-471
[10]万应明,高峻,董建平,等.多测井曲线的综合处理合成[J],石油地球物理勘探,2005,40(2):243-247
[11]吕瑞兰,吴铁军,于玲.采用不同小波母函数的阈值去噪[J],光谱学与光谱分析,2004,24(7):826-829
[12]金龙.旋转型压电行波超声马达的研究[D],南京:东南大学,1997
[13]UEHAS,TOMIKAWAY.Ultrasonic motor:theory and applications[M].Clarendon Press,1993.
[14]NICOLA LA piezoelectric motorussing flexural vibration of a thin piezoelectric membrance [J].IEEE Transaction on Ultrasonics,Ferroelectrics,and Frequency Control,1998,45(1):23-29.
[15]MALLAT S.Theory formulti-resolution signal decomposition:The wavelet representation[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[16]Stephane Mallat著,杨力华戴道清黄文良译.信号处理的小波导引[M].北京:机械工业出版社,2002
[17]杜文杰,元松.小波理论在路面平整度降噪分析中的应用[J],公路与汽运:51-54
[18]丰彦,高国荣.小波阈值消噪算法中分解层数的自适应确定[J],武汉大学学报,2005,51(2):11-14
[19]吕瑞兰,吴铁军,于玲.采用不同小波母函数的阈值去噪方法性能分析[J],光谱学与光谱分析,2004,7(24):826-829
[20]飞思科技产品研发中心.小波分析理论与Matlab7实现[M],电子工业出版社,2005,147
[21]飞思科技产品研发中心.小波分析理论与Matlab7实现[M],电子工业出版社,2005,33页
[22]余继峰,李增学.测井数据小波变换及其地质意义[J],中国矿业大学学报,2003,32(3):336-338
[23]Richard D,Qin S J.Joint diagnosis of process and sensor faults using principal component analysis [J],Control Engineering Practice,1998,6(4):457-4690
[24]Chiang L H,Russell E L,Braatz R D.Fault diagnosis in chemical process using fisher discriminant analysis,discdminant partial least squares,and principal component analysis[J],Chemometdcs and Intelligent Laboratory Systems,2000,50(2):243-252
[25]高朋,黄世祥.基于主成分分析法的地区现代化水平评价研究[J],市场周刊,2006:138-140
[26]贾传亮,胡发胜,孙颖.主成分分析法在产业关联度研究中的应用[J],运筹与管理,2006,15(2):73-76
[27]管昌生,王蕾蕾.主成分分析法在建筑企业评价中的应用[J],计算机与数字工程,2006,34(1):61-64
[28]王炜,马钦忠,吴耿锋,等.主成分分析法在地震预测中的应用研究[J],中国地震,2005,21(3):409-416
[29]王水献,杜卫东,董新光.新疆阿瓦提灌区土壤盐渍化状况的主成分分析[J],中国农村水利水电,2007(1):43-47
[30]马虹.主成分分析法在水质综合评价中的应用[J],南昌工程学院学报,2006,25(1):65-67
[31]范文,刘雪梅,高德彬,等.主成分分析法在地质灾害危险性综合评价中的应用[J],西安工程学院学报,2001,23(4):53-57
[32]朱小鸽.多重主成分分析及其在地质构造信息提取中的应用[J],遥感学报,2000,4(4):299-304
[33]陈方鸿,王贵文.塔里木盆底塔中地区志留系测井层序地层学研究[J],沉积学报,1999,17(1):58-62
[34]Bezdek J C.Pattern Recognition with Fuzzy Objective Function Algorithms[M].New York:Plenum Press,1981
[35]刘洪林,朱秋影,周振兴.模糊聚类分析及其在测井识别油气层中的应用[J],勘探地球物理进展,2005,28(6):425_428
[36]史海英.地震属性聚类分析技术在苏北某油田的应用[J].石油物探,2004,43:86-88
[37]张吉,张烈辉,陈军等.岔河集油田岔39块流动单元系统聚类划分及特征分析[J],河南石油,2004,18(6):33-36
[38]刘华,刘玉芳.系统聚类统计分析法在油气田开发中的应用[J],胜利油田职工大学学报,2006, 20(6):43-45
[39]杨建军,谢振乾,郑宁平.模糊聚类分析在西安市区域地壳稳定性评价中的应用[J],地质力学学报,2004,10(10):57-64
[40]曲永玲,陈新民.聚类分析法在土层划分中的应用[J],南京工业大学学报,2005,27(5):85-88
[42]苏金明,阮沈勇等,等.Matlab工程数学[M],电子工业出版社,2005,110
[43]金相郁.中国区域划分的层次聚类分析[J],城市规划汇刊,2004(2):23-28
[44]邵峰晶.数据挖掘原理与算法[M].北京:中国水利水电出版社,2003
[45]陈方鸿,王贵文.塔里木盆地塔中地区志留系测井层序地层学研究.沉积学报,1999,17(1):58-62.
[46]M.G克尔兹.测井分析中的图象处理[M],石油工业出版社,1992,113.
[47]黄智辉,地球物理测井资料在分析沉积环境中的应用[M],地址出版社,1989
[48]文成林.多尺度估计理论及方法研究[D].西安:西北工业大学博士论文,1999
[2]龚一鸣等,地层学基础与前沿[M],中国地质大学出版社,2000,12
[3]车卓吾.测井资料分析手册[M].北京:石油工业出版社,1995
[4]向导科技,MATLAB6.0程序设计与实例应用[M],中国铁道出版社,2001,1-17
[5]飞思科技产品研发中心,神经网络理论与MATLAB 7实现[M],电子工业出版社,2005.3
[6]王旭东等,MATLAB及其在FPGA中的应用[M],国防工业出版社,2006
[7](美)Edward B.Magrab,高会生等,MATLAB原理与工程应用[M],电子工业出版社,2006.4--11
[8]苏金明等,MATLAB工程数学[M],电子工业出版社,2005
[9]刘明寿,周晓明,戴国俊,等.用归一化方法评价科技期刊学术质量与常规评定结果的相关性分析[J],编辑学报,2006,18(6):468-471
[10]万应明,高峻,董建平,等.多测井曲线的综合处理合成[J],石油地球物理勘探,2005,40(2):243-247
[11]吕瑞兰,吴铁军,于玲.采用不同小波母函数的阈值去噪[J],光谱学与光谱分析,2004,24(7):826-829
[12]金龙.旋转型压电行波超声马达的研究[D],南京:东南大学,1997
[13]UEHAS,TOMIKAWAY.Ultrasonic motor:theory and applications[M].Clarendon Press,1993.
[14]NICOLA LA piezoelectric motorussing flexural vibration of a thin piezoelectric membrance [J].IEEE Transaction on Ultrasonics,Ferroelectrics,and Frequency Control,1998,45(1):23-29.
[15]MALLAT S.Theory formulti-resolution signal decomposition:The wavelet representation[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[16]Stephane Mallat著,杨力华戴道清黄文良译.信号处理的小波导引[M].北京:机械工业出版社,2002
[17]杜文杰,元松.小波理论在路面平整度降噪分析中的应用[J],公路与汽运:51-54
[18]丰彦,高国荣.小波阈值消噪算法中分解层数的自适应确定[J],武汉大学学报,2005,51(2):11-14
[19]吕瑞兰,吴铁军,于玲.采用不同小波母函数的阈值去噪方法性能分析[J],光谱学与光谱分析,2004,7(24):826-829
[20]飞思科技产品研发中心.小波分析理论与Matlab7实现[M],电子工业出版社,2005,147
[21]飞思科技产品研发中心.小波分析理论与Matlab7实现[M],电子工业出版社,2005,33页
[22]余继峰,李增学.测井数据小波变换及其地质意义[J],中国矿业大学学报,2003,32(3):336-338
[23]Richard D,Qin S J.Joint diagnosis of process and sensor faults using principal component analysis [J],Control Engineering Practice,1998,6(4):457-4690
[24]Chiang L H,Russell E L,Braatz R D.Fault diagnosis in chemical process using fisher discriminant analysis,discdminant partial least squares,and principal component analysis[J],Chemometdcs and Intelligent Laboratory Systems,2000,50(2):243-252
[25]高朋,黄世祥.基于主成分分析法的地区现代化水平评价研究[J],市场周刊,2006:138-140
[26]贾传亮,胡发胜,孙颖.主成分分析法在产业关联度研究中的应用[J],运筹与管理,2006,15(2):73-76
[27]管昌生,王蕾蕾.主成分分析法在建筑企业评价中的应用[J],计算机与数字工程,2006,34(1):61-64
[28]王炜,马钦忠,吴耿锋,等.主成分分析法在地震预测中的应用研究[J],中国地震,2005,21(3):409-416
[29]王水献,杜卫东,董新光.新疆阿瓦提灌区土壤盐渍化状况的主成分分析[J],中国农村水利水电,2007(1):43-47
[30]马虹.主成分分析法在水质综合评价中的应用[J],南昌工程学院学报,2006,25(1):65-67
[31]范文,刘雪梅,高德彬,等.主成分分析法在地质灾害危险性综合评价中的应用[J],西安工程学院学报,2001,23(4):53-57
[32]朱小鸽.多重主成分分析及其在地质构造信息提取中的应用[J],遥感学报,2000,4(4):299-304
[33]陈方鸿,王贵文.塔里木盆底塔中地区志留系测井层序地层学研究[J],沉积学报,1999,17(1):58-62
[34]Bezdek J C.Pattern Recognition with Fuzzy Objective Function Algorithms[M].New York:Plenum Press,1981
[35]刘洪林,朱秋影,周振兴.模糊聚类分析及其在测井识别油气层中的应用[J],勘探地球物理进展,2005,28(6):425_428
[36]史海英.地震属性聚类分析技术在苏北某油田的应用[J].石油物探,2004,43:86-88
[37]张吉,张烈辉,陈军等.岔河集油田岔39块流动单元系统聚类划分及特征分析[J],河南石油,2004,18(6):33-36
[38]刘华,刘玉芳.系统聚类统计分析法在油气田开发中的应用[J],胜利油田职工大学学报,2006, 20(6):43-45
[39]杨建军,谢振乾,郑宁平.模糊聚类分析在西安市区域地壳稳定性评价中的应用[J],地质力学学报,2004,10(10):57-64
[40]曲永玲,陈新民.聚类分析法在土层划分中的应用[J],南京工业大学学报,2005,27(5):85-88
[42]苏金明,阮沈勇等,等.Matlab工程数学[M],电子工业出版社,2005,110
[43]金相郁.中国区域划分的层次聚类分析[J],城市规划汇刊,2004(2):23-28
[44]邵峰晶.数据挖掘原理与算法[M].北京:中国水利水电出版社,2003
[45]陈方鸿,王贵文.塔里木盆地塔中地区志留系测井层序地层学研究.沉积学报,1999,17(1):58-62.
[46]M.G克尔兹.测井分析中的图象处理[M],石油工业出版社,1992,113.
[47]黄智辉,地球物理测井资料在分析沉积环境中的应用[M],地址出版社,1989
[48]文成林.多尺度估计理论及方法研究[D].西安:西北工业大学博士论文,1999