核主成分分析法在测井浊积岩岩性识别中的应用
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摘要
在复杂岩性油气藏储层评价中,如何合理利用测井曲线信息判别岩性是一个难题,东营凹陷董集洼陷浊积岩岩性复杂,采用常规交会图法以及主成分分析法都难以有效地识别岩性。为了解决这一问题,基于粒子群算法以及核函数理论,结合该区的测井响应特征,采用核主成分分析法,建立新的主成分计算方法,选取该区实测的自然伽马测井(GR)、声波时差测井(AC)、中子孔隙度测井(CNL)、密度测井(DEN)、原状地层电阻率(RT)构建出五个主成分变量,其中前两个主成分变量累积贡献率达到了93.83%,可以有效地代替原始多维测井信息。实例分析结果表明,利用前两个主成分变量主成分进行交会分析,可以有效地识别浊积岩的岩性,并将该方法在研究区进行了试验,岩性识别准确率达到了90%,较传统方法具有更高的岩性识别精度,取得了良好的应用效果。
It is difficult to identify the lithology on welllogging data in the evaluation of complex lithologic reservoirs.Due to complexity of turbidite reservoirs in Dongji Sag,Dongying Depression,conventional cross-plot and principal component analysis methods fail to identify their lithology.In order to solve this problem,based on particle swarm optimization and kernel function theory and combining with log response characteristics of the area,an improved principal component analysis method is used to establish a new principal component calculation.Five principal component variables are constructed with measured natural gamma-ray logging(GR),acoustic logging(AC),compensated neutron porosity logging(CNL),density logging(DEN),and virgin zone resistivity(RT)of reservoirs.The accumulate contribution rate of the first two principal component variables reached 93.83%,which can effectively replace the original multi-dimensional logging information.The proposed method is tested in the study area.Based on our application results,this proposed method can effectively identify the lithology of turbidite reservoirs,and its identification rate reaches up to 90%.
It is difficult to identify the lithology on welllogging data in the evaluation of complex lithologic reservoirs.Due to complexity of turbidite reservoirs in Dongji Sag,Dongying Depression,conventional cross-plot and principal component analysis methods fail to identify their lithology.In order to solve this problem,based on particle swarm optimization and kernel function theory and combining with log response characteristics of the area,an improved principal component analysis method is used to establish a new principal component calculation.Five principal component variables are constructed with measured natural gamma-ray logging(GR),acoustic logging(AC),compensated neutron porosity logging(CNL),density logging(DEN),and virgin zone resistivity(RT)of reservoirs.The accumulate contribution rate of the first two principal component variables reached 93.83%,which can effectively replace the original multi-dimensional logging information.The proposed method is tested in the study area.Based on our application results,this proposed method can effectively identify the lithology of turbidite reservoirs,and its identification rate reaches up to 90%.
引文
[1] Busch J M,Fortney W G,Berry L N.Determination of lithology from well logs by statistical analysis[J].SPE Formation Evaluation,1987,2(4):412-418.
[2] Doveton J H.Geologic Log Analysis Using Computer Methods[M].AAGP,Tulsa,1994.
[3] 刘爱疆,左烈,李景景,等.主成分分析法在碳酸盐岩岩性识别中的应用——以YH地区寒武系碳酸盐岩储层为例[J].石油与天然气地质,2013,34(2):192-196.LIU Aijiang,ZUO lie,LI Jingjing,et al.Application of principal component analysis in carbonate lithology identification:a case study of the Cambrian carbonate reservoir in YH field[J].Oil & Gas Geology,2013,34(2):192-196.
[4] 潘保芝,李舟波,付有升,等.测井资料在松辽盆地火成岩岩性识别和储层评价中的应用[J].石油物探,2009,48(1):48-52.PAN Baozhi,LI Zhoubo,FU Yousheng,et al.Application of logging data in lithology identification and reservoir evaluation of igneous rock in Songliao basin[J].Geophysical Prospecting for Petroleum,2009,48(1):48-52.
[5] Samuel J R,Fang J H,Karra C L,et al.Determination of lithology from well logs using a neural network[J].AAPG Bulletin,1992,76(5):731-739.
[6] 吴磊,徐怀民,季汉成.基于交会图和多元统计法的神经网络技术在火山岩识别中的应用[J].石油地球物理勘探,2006,41(1):81-86.WU Lei,XU Huaimin,JI Hancheng.Application of neural networks technique based on crossplot and multielement statistics to recognition of volcanic rocks[J].Oil Geophysical Prospecting,2006,41(1):81-86.
[7] 张翔,肖小玲,严良俊,等.基于模糊支持向量机方法的岩性识别[J].石油天然气学报,2009,31(6):115-118.ZHANG Xiang,XIAO Xiaoling,YAN Liangjun,et al.Lithologic identification based on fuzzy support vector machines[J].Journal of Oil and Gas Technology,2009,31(6):115-118.
[8] 吴施楷,曹俊兴.基于连续限制玻尔兹曼机的支持向量机岩性识别方法[J].地球物理学进展,2016,31(2):821-828.WU Shikai,CAO Junxing.Lithology identification method based on continuous restricted Boltzmann machine and support vector machine[J].Progress in Geo-physics,2016,31(2):821-828.
[9] 葛新民,范卓颖,范宜仁,等.基于核主成分—小波能谱分析的复杂储层油水界面预测[J].中南大学学报(自然科学版),2015,46(5):1747-1753.GE Xinmin,FAN Zhuoying,FAN Yiren,et al.Oil/water contact prediction of complex reservoir using kernel principal component analysis and wavelet po-wer spectrum analysis[J].Journal of Central South University (Science and Technology),2015,46(5):1747-1753.
[10] 陈钢花,王军,程探探,等.粒子群算法在砂砾岩体岩性识别中的应用[J].测井技术,2015,39(2):171-174.CHEN Ganghua,WANG Jun,CHENG Tantan,et al.Application of particle swarm optimization to glute-nite lithology identification[J].Well Logging Techno-logy,2015,39(2):171-174.
[11] 于正军.灰质背景下浊积岩储层地震响应特征及识别方法——以东营凹陷董集洼陷为例[J].油气地质与采收率,2014,21(2):95-97.YU Zhengjun.Seismic response characteristics and recognition method of turbidity under carbonate depositional environment:a case in Dongji sag of Dongying sag[J].Petroleum Geology and Recovery Efficiency,2014,21(2):95-97.
[12] 周游,高刚,桂志先,等.灰质发育背景下识别浊积岩优质储层的技术研究——以东营凹陷董集洼陷为例[J].物探与化探,2017,41(5):899-906.ZHOU You,GAO Gang,GUI Zhixian,et al.Study on the identification of turbidite high-quality reservoirs under gray background:a case study in Dongji sag of Dongying depression[J].Geophysical and Geochemical Exploration,2017,41(5):899-906.
[13] 杨兆栓,林畅松,尹宏,等.主成分分析在塔中地区奥陶系鹰山组碳酸盐岩岩性识别中的应用[J].天然气地球科学,2015,26(1):54-59.YANG Zhaoshuan,LIN Changsong,YIN Hong,et al.Application of principal component analysis in carbo-nate lithology identification of the Ordovician Ying-shan formation in Tazhong area[J].Natural Gas Geoscience,2015,26(1):54-59.
[14] 史才旺,何兵寿.基于主成分分析和梯度重构的全波形反演[J].石油地球物理勘探,2018,53(1):95-104.SHI Caiwang,HE Bingshou.Full waveform inversion based on principal component analysis and gradient reconstruction[J].Oil Geophysical Prospecting,2018,53(1):95-104.
[15] 赵京,李立明.基于主成分分析法和核主成分分析法的机器人全域性能综合评价[J].北京工业大学学报,2014,40(12):1763-1769.ZHAO Jing,LI Liming.Comprehensive evaluation of robotic global performance based on principal component analysis and kernel principal component analysis[J].Journal of Beijing University of Technology,2014,40(12):1763-1769.
[16] 殷俊,周静波,金忠.基于余弦角距离的主成分分析与核主成分分析[J].计算机工程与应用,2011,47(3):9-12.YIN Jun,ZHOU Jingbo,JIN Zhong.Principal component analysis and kernel principal component analysis based on co-sine angle distance[J].Computer Engineering and Applications,2011,47(3):9-12.
[17] 郑静静,王延光,杜磊,等.基于概率核主成分分析的属性优化方法及其应用[J].石油地球物理勘探,2014,49(3):567-571.ZHENG Jingjing,WANG Yanguang,DU Lei,et al.Attribute optimization based on the probability kernel principal component analysis[J].Oil Geophysical Prospecting,2014,49(3):567-571.
[18] 印兴耀,孔国英,张广智.基于核主成分分析的地震属性优化方法及应用[J].石油地球物理勘探,2008,43(2):179-183.YIN Xingyao,KONG Guoying,ZHANG Guangzhi.Seismic attributes optimization based on kernel principal component analysis (KPCA) and application[J].Oil Geophysical Prospecting,2008,43(2):179-183.
[19] 陈斌,陆从德,刘光鼎.基于核主成分分析的时间域航空电磁去噪方法[J].地球物理学报,2014,57(1):295-302.CHEN Bin,LU Congde,LIU Guangding.A denoising method based on kernel principal component analysis for airborne time domain electromagnetic data[J].Chinese Journal of Geophysics,2014,57(1):295-302.
[20] 胡嘉蕊,吕震宙.基于核主成分分析的多输出模型确认方法[J].北京航空航天大学学报,2017,43(7):1470-1480.HU Jiarui,LYU Zhenzhou.Model validation method with multivariate output based on kernel principal component analysis[J].Journal of Beijing University of Aeronautics and Astronautics,2017,43(7):1470-1480.
[21] 潘新朋,张广智,印兴耀.岩石物理驱动的储层裂缝参数与物性参数概率地震反演方法[J].地球物理学报,2018,61(2):683-696.PAN Xinpeng,ZHANG Guangzhi,YIN Xingyao.Probabilistic seismic inversion for reservoir fracture and petrophysical parameters driven by rock-physics models[J].Chinese Journal of Geophysics,2018,61(2):683-696.
[22] Sch?lkopf B,Smola A,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation,1998,10(5):1299-1319.
[23] Sch?lkopf B,Smola A,Muller K R.Kermel principal component analysis[J].Advances in Kernel Methods-Support Vector Learning,2009,27(4):555-559.
[24] Jolliffe I T.Principal Component Analysis[M].Springer-Verlag,Berlin,2002,20-24.
[25] 程国建,郭瑞华.PSO-LSSVM分类模型在岩性识别中的应用[J].西安石油大学学报(自然科学版),2010,25(1):96-99.CHENG Guojian,GUO Ruihua.Application of PSO-LSSVM classification model in logging lithology reco-gnition[J].Journal of Xi’an Shiyou University (Na-tural Science Edition),2010,25(1):96-99.
[26] 马海,王延江,胡睿等.测井岩性识别新方法研究[J].地球物理学进展,2009,24(1):263-269.MA Hai,WANG Yanjiang,HU Rui,et al.A novel method for well logging lithologic identification.Progress in Geophysics[J],2009,24(1):263-269.
[27] 戴世立.徐家围子断陷营城组火山岩岩性、储层岩石物理弹性参数特征分析[J].石油地球物理勘探,2018,53(1):122-128.DAI Shili.Volcanic lithology and reservoir identification based elastic wave characteristics analysis in Yingcheng Formation,Xujiaweizi Depression[J].Oil Geophysical Prospecting,2018,53(1):122-128.
[28] 刘毅,陆正元,吕晶,等.主成分分析法在泥页岩地层岩性识别中的应用[J].断块油气田,2017,24(3):360-363.LIU Yi,LU Zhengyuan,LYU Jing,et al.Application of principal component analysis method in lithology identification for shale-formation[J].Fault-Block Oil & Gas Field,2017,24(3):360-363.
[29] 关涛.基于交会图和贝叶斯聚类分析法的岩性识别方法[J].科学技术与工程,2013,13(4):976-979.GUAN Tao.Method of lithololgic identification based on crossplot and Bayseian cluster analysis algorithm[J].Science Technology and Engineering,2013,13(4):976-979.
[2] Doveton J H.Geologic Log Analysis Using Computer Methods[M].AAGP,Tulsa,1994.
[3] 刘爱疆,左烈,李景景,等.主成分分析法在碳酸盐岩岩性识别中的应用——以YH地区寒武系碳酸盐岩储层为例[J].石油与天然气地质,2013,34(2):192-196.LIU Aijiang,ZUO lie,LI Jingjing,et al.Application of principal component analysis in carbonate lithology identification:a case study of the Cambrian carbonate reservoir in YH field[J].Oil & Gas Geology,2013,34(2):192-196.
[4] 潘保芝,李舟波,付有升,等.测井资料在松辽盆地火成岩岩性识别和储层评价中的应用[J].石油物探,2009,48(1):48-52.PAN Baozhi,LI Zhoubo,FU Yousheng,et al.Application of logging data in lithology identification and reservoir evaluation of igneous rock in Songliao basin[J].Geophysical Prospecting for Petroleum,2009,48(1):48-52.
[5] Samuel J R,Fang J H,Karra C L,et al.Determination of lithology from well logs using a neural network[J].AAPG Bulletin,1992,76(5):731-739.
[6] 吴磊,徐怀民,季汉成.基于交会图和多元统计法的神经网络技术在火山岩识别中的应用[J].石油地球物理勘探,2006,41(1):81-86.WU Lei,XU Huaimin,JI Hancheng.Application of neural networks technique based on crossplot and multielement statistics to recognition of volcanic rocks[J].Oil Geophysical Prospecting,2006,41(1):81-86.
[7] 张翔,肖小玲,严良俊,等.基于模糊支持向量机方法的岩性识别[J].石油天然气学报,2009,31(6):115-118.ZHANG Xiang,XIAO Xiaoling,YAN Liangjun,et al.Lithologic identification based on fuzzy support vector machines[J].Journal of Oil and Gas Technology,2009,31(6):115-118.
[8] 吴施楷,曹俊兴.基于连续限制玻尔兹曼机的支持向量机岩性识别方法[J].地球物理学进展,2016,31(2):821-828.WU Shikai,CAO Junxing.Lithology identification method based on continuous restricted Boltzmann machine and support vector machine[J].Progress in Geo-physics,2016,31(2):821-828.
[9] 葛新民,范卓颖,范宜仁,等.基于核主成分—小波能谱分析的复杂储层油水界面预测[J].中南大学学报(自然科学版),2015,46(5):1747-1753.GE Xinmin,FAN Zhuoying,FAN Yiren,et al.Oil/water contact prediction of complex reservoir using kernel principal component analysis and wavelet po-wer spectrum analysis[J].Journal of Central South University (Science and Technology),2015,46(5):1747-1753.
[10] 陈钢花,王军,程探探,等.粒子群算法在砂砾岩体岩性识别中的应用[J].测井技术,2015,39(2):171-174.CHEN Ganghua,WANG Jun,CHENG Tantan,et al.Application of particle swarm optimization to glute-nite lithology identification[J].Well Logging Techno-logy,2015,39(2):171-174.
[11] 于正军.灰质背景下浊积岩储层地震响应特征及识别方法——以东营凹陷董集洼陷为例[J].油气地质与采收率,2014,21(2):95-97.YU Zhengjun.Seismic response characteristics and recognition method of turbidity under carbonate depositional environment:a case in Dongji sag of Dongying sag[J].Petroleum Geology and Recovery Efficiency,2014,21(2):95-97.
[12] 周游,高刚,桂志先,等.灰质发育背景下识别浊积岩优质储层的技术研究——以东营凹陷董集洼陷为例[J].物探与化探,2017,41(5):899-906.ZHOU You,GAO Gang,GUI Zhixian,et al.Study on the identification of turbidite high-quality reservoirs under gray background:a case study in Dongji sag of Dongying depression[J].Geophysical and Geochemical Exploration,2017,41(5):899-906.
[13] 杨兆栓,林畅松,尹宏,等.主成分分析在塔中地区奥陶系鹰山组碳酸盐岩岩性识别中的应用[J].天然气地球科学,2015,26(1):54-59.YANG Zhaoshuan,LIN Changsong,YIN Hong,et al.Application of principal component analysis in carbo-nate lithology identification of the Ordovician Ying-shan formation in Tazhong area[J].Natural Gas Geoscience,2015,26(1):54-59.
[14] 史才旺,何兵寿.基于主成分分析和梯度重构的全波形反演[J].石油地球物理勘探,2018,53(1):95-104.SHI Caiwang,HE Bingshou.Full waveform inversion based on principal component analysis and gradient reconstruction[J].Oil Geophysical Prospecting,2018,53(1):95-104.
[15] 赵京,李立明.基于主成分分析法和核主成分分析法的机器人全域性能综合评价[J].北京工业大学学报,2014,40(12):1763-1769.ZHAO Jing,LI Liming.Comprehensive evaluation of robotic global performance based on principal component analysis and kernel principal component analysis[J].Journal of Beijing University of Technology,2014,40(12):1763-1769.
[16] 殷俊,周静波,金忠.基于余弦角距离的主成分分析与核主成分分析[J].计算机工程与应用,2011,47(3):9-12.YIN Jun,ZHOU Jingbo,JIN Zhong.Principal component analysis and kernel principal component analysis based on co-sine angle distance[J].Computer Engineering and Applications,2011,47(3):9-12.
[17] 郑静静,王延光,杜磊,等.基于概率核主成分分析的属性优化方法及其应用[J].石油地球物理勘探,2014,49(3):567-571.ZHENG Jingjing,WANG Yanguang,DU Lei,et al.Attribute optimization based on the probability kernel principal component analysis[J].Oil Geophysical Prospecting,2014,49(3):567-571.
[18] 印兴耀,孔国英,张广智.基于核主成分分析的地震属性优化方法及应用[J].石油地球物理勘探,2008,43(2):179-183.YIN Xingyao,KONG Guoying,ZHANG Guangzhi.Seismic attributes optimization based on kernel principal component analysis (KPCA) and application[J].Oil Geophysical Prospecting,2008,43(2):179-183.
[19] 陈斌,陆从德,刘光鼎.基于核主成分分析的时间域航空电磁去噪方法[J].地球物理学报,2014,57(1):295-302.CHEN Bin,LU Congde,LIU Guangding.A denoising method based on kernel principal component analysis for airborne time domain electromagnetic data[J].Chinese Journal of Geophysics,2014,57(1):295-302.
[20] 胡嘉蕊,吕震宙.基于核主成分分析的多输出模型确认方法[J].北京航空航天大学学报,2017,43(7):1470-1480.HU Jiarui,LYU Zhenzhou.Model validation method with multivariate output based on kernel principal component analysis[J].Journal of Beijing University of Aeronautics and Astronautics,2017,43(7):1470-1480.
[21] 潘新朋,张广智,印兴耀.岩石物理驱动的储层裂缝参数与物性参数概率地震反演方法[J].地球物理学报,2018,61(2):683-696.PAN Xinpeng,ZHANG Guangzhi,YIN Xingyao.Probabilistic seismic inversion for reservoir fracture and petrophysical parameters driven by rock-physics models[J].Chinese Journal of Geophysics,2018,61(2):683-696.
[22] Sch?lkopf B,Smola A,Muller K R.Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation,1998,10(5):1299-1319.
[23] Sch?lkopf B,Smola A,Muller K R.Kermel principal component analysis[J].Advances in Kernel Methods-Support Vector Learning,2009,27(4):555-559.
[24] Jolliffe I T.Principal Component Analysis[M].Springer-Verlag,Berlin,2002,20-24.
[25] 程国建,郭瑞华.PSO-LSSVM分类模型在岩性识别中的应用[J].西安石油大学学报(自然科学版),2010,25(1):96-99.CHENG Guojian,GUO Ruihua.Application of PSO-LSSVM classification model in logging lithology reco-gnition[J].Journal of Xi’an Shiyou University (Na-tural Science Edition),2010,25(1):96-99.
[26] 马海,王延江,胡睿等.测井岩性识别新方法研究[J].地球物理学进展,2009,24(1):263-269.MA Hai,WANG Yanjiang,HU Rui,et al.A novel method for well logging lithologic identification.Progress in Geophysics[J],2009,24(1):263-269.
[27] 戴世立.徐家围子断陷营城组火山岩岩性、储层岩石物理弹性参数特征分析[J].石油地球物理勘探,2018,53(1):122-128.DAI Shili.Volcanic lithology and reservoir identification based elastic wave characteristics analysis in Yingcheng Formation,Xujiaweizi Depression[J].Oil Geophysical Prospecting,2018,53(1):122-128.
[28] 刘毅,陆正元,吕晶,等.主成分分析法在泥页岩地层岩性识别中的应用[J].断块油气田,2017,24(3):360-363.LIU Yi,LU Zhengyuan,LYU Jing,et al.Application of principal component analysis method in lithology identification for shale-formation[J].Fault-Block Oil & Gas Field,2017,24(3):360-363.
[29] 关涛.基于交会图和贝叶斯聚类分析法的岩性识别方法[J].科学技术与工程,2013,13(4):976-979.GUAN Tao.Method of lithololgic identification based on crossplot and Bayseian cluster analysis algorithm[J].Science Technology and Engineering,2013,13(4):976-979.
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