基于经验模态分解和能量熵判别的火成岩岩性识别方法——以春风油田石炭系火成岩储层为例
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摘要
火成岩储层具有"喷发模式多样化、岩性成因多元化、矿物组合多变化"的特征,岩性精细识别难度大,严重制约了储层参数的计算及后续油气开发。以准噶尔盆地春风油田石炭系储层为例,结合岩心和薄片等分析测试资料将储层发育的岩性分为5类:玄武岩、玄武质安山岩、安山岩、凝灰岩和火山角砾岩。在明确不同岩性测井响应特征的基础上,采用"逐级剥离"的思想,利用交会图法识别出了凝灰岩和火山角砾岩。针对较难识别的火山熔岩,引入经验模态分解算法将常规测井资料转化为多个频带的本征模态函数集,得到了各熔岩不同测井参数经验模态函数的能量熵,应用判别算法实现了火成岩岩性的精细识别。区块应用结果表明:该方法岩性识别整体符合率高达93.7%,有效提高了岩性识别精度。
Igneous reservoirs are characterized by diversity of eruption patterns and lithology geneses,and variation in mineralogy assemblage.It is really difficult to identify lithology in details,which in turn will greatly hinder the correct calculation of reservoir parameters,as well as subsequent hydrocarbon development strategies. Therefore,the study took a case study of the carboniferous igneous reservoirs in Chunfeng oilfield,Junggar Basin. The lithological categories of the reservoirs are basalt,basaltic andesite,andesite,tuff and volcanic breccias. A combination of core,thin section and other tests' data were used in the study.Then the logging response characteristics of different lithologies were clarified.The tuff and volcanic breccia were identified with the cross plot technique and"progressive stripping"concept. For the hard-to-identify volcanic lava,we used the empirical mode decomposition algorithm to convert the conventional logging data into multiple band sets of intrinsic mode functions,and attained the energy entropy of empirical mode function of various logging parameters for lavas.Then the precise identification of igneous lithologies was realized by using the discriminant algorithm.In applying the method to the block,we found that the overall matching rate of the proposed method is 93. 7%,which has greatly improved the accuracy of lithological identification.
Igneous reservoirs are characterized by diversity of eruption patterns and lithology geneses,and variation in mineralogy assemblage.It is really difficult to identify lithology in details,which in turn will greatly hinder the correct calculation of reservoir parameters,as well as subsequent hydrocarbon development strategies. Therefore,the study took a case study of the carboniferous igneous reservoirs in Chunfeng oilfield,Junggar Basin. The lithological categories of the reservoirs are basalt,basaltic andesite,andesite,tuff and volcanic breccias. A combination of core,thin section and other tests' data were used in the study.Then the logging response characteristics of different lithologies were clarified.The tuff and volcanic breccia were identified with the cross plot technique and"progressive stripping"concept. For the hard-to-identify volcanic lava,we used the empirical mode decomposition algorithm to convert the conventional logging data into multiple band sets of intrinsic mode functions,and attained the energy entropy of empirical mode function of various logging parameters for lavas.Then the precise identification of igneous lithologies was realized by using the discriminant algorithm.In applying the method to the block,we found that the overall matching rate of the proposed method is 93. 7%,which has greatly improved the accuracy of lithological identification.
引文
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[15]刘喜顺,许杰,张晓平.准噶尔盆地西北缘石炭系火山岩岩相特征及相模式[J].新疆地质,2010,28(1):73-76.Liu Xishun,Xu Jie,Zhang Xiaoping.The volcano lithology characteristic and faces model in north-west of jungger basin[J].Xinjiang Geology,2010,28(1):73-76.
[16]邵忍平,曹精明,李永龙.基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击,2012,31(8):96-101.Shao Renping,Cao Jingming,Li Yonglong.Gear fault pattern identification and diagnosis using time-frequency analysis and wavelet threshold de-noising based on EMD[J].Journal of Vibration And Shock,2012,31(8):96-101.
[17]钱昌松,刘代志,刘志刚,等.基于递归高通滤波的经验模态分解及其在地震信号分析中的应用[J].地球物理学报,2010,53(5):1215-1225.Qian Changsong,Liu Daizhi,Liu Zhigang,et al.EMD based on recursive high pass filter and application on seismic signal analysis[J].Chines Journal of Geophysics,2010,53(5):1215-1225.
[18]孔丁科,汪国昭.基于EMD的快速活动轮廓图像分割算法[J].电子与信息学报,2010,32:1094-1099.Kong Dingke,Wang Guozhao.Fast active contour model for image segmentation based on EMD[J].Journal of Electronics&Information Technology,2010,32:1094-1099.
[19]杨宇.基于EMD和支持向量机的旋转机械故障诊断方法研究[D].湖南大学,2005.Yang Yu.Research on fault diagnosis methods for rotating machinery based on empirical mode decomposition and support vector machine[D].Hunan University,2005.
[20]Loh C H,Wu T C,Huang N E,et al.Application of the empirical mode decomposition:hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses[J].Bulletin of the Seismological Society of America,2001,91(5):1339-1357.
[2]江怀友,鞠斌山,江良冀,等.世界火成岩油气勘探开发现状与展望[J].特种油气藏,2011,18(2):1-6,135.Jiang Huaiyou,Ju Binshan,Jiang Liangji,et al.Current situation and prospect of global igneous reservoir exploration and development[J].Special Oil and Gas Reservoirs,2011,18(2):1-6,135.
[3]卫平生,潘建国,谭开俊,等.世界典型火成岩油气藏储层[M].北京:石油工业出版社,2015.Wei Pingsheng,Pan Jianguo,Tan Kaijun,et al.Typical oil&gas res-ervoirs of igneous rocks in the World[M].Beijing:Petroleum Industry Press,2015.
[4]Sanyal S K,Juprasert S,Jusbasche M.An evaluation of rhyolite-basalt-volcanic ash sequence from well logs[R].Tulsa,Oklahorma:SPWLA 20th Annual Logging Symposium,1979.
[5]Khatchikian A.Log evaluation of oil-bearing igneous rocks[R].Corpus Christ,Texas:SP WLA 23 th Annual Logging Symposium,1982
[6]赵武生,谭伏霖,王志章,等.准噶尔盆地腹部火成岩岩性识别[J].天然气工业,2010,30(2):21-25.Zhao Wusheng,Tan Fulin,Wang Zhizhang,et al.Identification of the lithology of igneous rocks in central of the Junggar Basin[J].Natural Gas Industry,2010,30(2):21-25.
[7]潘保芝,闫桂京,吴海波.对应分析确定松辽盆地北部深层火成岩岩性[J].大庆石油地质与开发,2003,22(1):7-9.Pan Baozhi,Yan Guijing,Wu Haibo.Determining lithology of igneous rocks in deep northern songliao basin using correspondence analysis[J].Petroleum Geology&Oilfield Development in Daqing,2003,22(1):7-9.
[8]朱怡翔,石广仁.火山岩岩性的支持向量机识别[J].石油学报,2013,34(2):312-322.Zhu Yixiang,Shi Guangren.Identification of lithologic characteristicsof volcanic rocks by support vector machine[J].Acta Petrolei Sinica,2013,34(2):312-322.
[9]张平,潘保芝,张莹,等.自组织神经网络在火成岩岩性识别中的应用[J].石油物探,2009,48(1):53-56.Zhang Ping,Pan Baozhi,Zhang Ying,et al.Application of self-organization maps network in identifying the lithology of igneous rock[J].Geophysical Prospecting for pertoleum,2009,48(1):53-56.
[10]刘传平,郑建东,杨景强.徐深气田深层火山岩测井岩性识别方法[J].石油学报,2006,27:62-65.Liu Chuanping,Zheng Jiandong,Yang Jingqiang.Lithology identification of well Iogging for deep volcanic reservoir in XUshen Ga S Field[J].Acta Petrolei Sinica,006,27:62-65.
[11]谭伏霖,王志章,隆山,等.准噶尔盆地滴西地区火成岩岩性识别方法研究[J].石油天然气学报(江汉石油学院学报),2011,33(4):92-95.Tan Fulin,Wang Zhizhang,Long Shan,et al.Igneous rock lithologic identification in Dixi area of Jungger Basin[J].Journal of Oil and Gas Technology,2011,33(4):92-95.
[12]陈钢花,范宜仁,代诗华.火山岩储层测井评价技术[J].中国海上油气(地质),2000,14(6):422-428.Chen Ganghua,Fan Yiren,Dai Shihua.An evaluaion technique for Volcanic Reservoir using well logging data[J].China Offshore Oil and Gas(Geology),2000,14(6):422-428.
[13]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Mathematical Physical&Engineering Sciences,1998,454(1971):903-995.
[14]徐小刚,徐冠雷,王孝通,等.经验模式分解(EMD)及其应用[J].电子学报,2009,37(3):581-585.Xu Xiaogang,Xu Guanlei,Wang Xiaotong,et al.Empirical mode decomposition and its application[J].Acta Electronica Sinica,2009,37(3):581-585.
[15]刘喜顺,许杰,张晓平.准噶尔盆地西北缘石炭系火山岩岩相特征及相模式[J].新疆地质,2010,28(1):73-76.Liu Xishun,Xu Jie,Zhang Xiaoping.The volcano lithology characteristic and faces model in north-west of jungger basin[J].Xinjiang Geology,2010,28(1):73-76.
[16]邵忍平,曹精明,李永龙.基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击,2012,31(8):96-101.Shao Renping,Cao Jingming,Li Yonglong.Gear fault pattern identification and diagnosis using time-frequency analysis and wavelet threshold de-noising based on EMD[J].Journal of Vibration And Shock,2012,31(8):96-101.
[17]钱昌松,刘代志,刘志刚,等.基于递归高通滤波的经验模态分解及其在地震信号分析中的应用[J].地球物理学报,2010,53(5):1215-1225.Qian Changsong,Liu Daizhi,Liu Zhigang,et al.EMD based on recursive high pass filter and application on seismic signal analysis[J].Chines Journal of Geophysics,2010,53(5):1215-1225.
[18]孔丁科,汪国昭.基于EMD的快速活动轮廓图像分割算法[J].电子与信息学报,2010,32:1094-1099.Kong Dingke,Wang Guozhao.Fast active contour model for image segmentation based on EMD[J].Journal of Electronics&Information Technology,2010,32:1094-1099.
[19]杨宇.基于EMD和支持向量机的旋转机械故障诊断方法研究[D].湖南大学,2005.Yang Yu.Research on fault diagnosis methods for rotating machinery based on empirical mode decomposition and support vector machine[D].Hunan University,2005.
[20]Loh C H,Wu T C,Huang N E,et al.Application of the empirical mode decomposition:hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses[J].Bulletin of the Seismological Society of America,2001,91(5):1339-1357.