基于三参数小波的频谱分解方法
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摘要
首先阐述了频谱分解方法的基本原理,然后介绍了三参数小波,重点研究了每个参数对小波的影响,随后设计了正演模型,利用三参数小波进行时频分析;最后利用基于三参数小波的频谱分解方法预测储层,发现三参数小波能更好地显示细微的地质信息。模型测试及实际地震资料应用表明:①三参数小波的灵活性高,小波的调制频率σ影响小波的震荡程度。能量衰减因子τ影响小波的宽窄,当τ较大时,σ对小波的影响减弱。能量延迟因子β的影响颇为复杂,当其为周期的整数倍时,小波只产生时移,可以匹配零相位子波;当其为周期的非整数倍时,小波不仅产生时移,而且产生相位延迟,可以匹配非零相位子波。同时需要注意,β非零会造成河道埋深解释错误。②三参数小波变换较Morlet小波变换具有较好的时频分辨率,能更好地刻画薄互层内细微的地质沉积结构。③实际处理中可将从目的层段提取的地震子波与三参数小波做相关,优选相关性大的参数组合,从而获得最佳的三参数小波。
We first describe the basic principle of spec-trum decomposition,then introduce three-parameter wavelet,study the influence of each parameter on wavelet.After that we conduct time-frequency analysis with three parameters based on forward modeling.Finally we perform reservoir characterization with three-parameter wavelet.It is found that subtle geological information can be highlighted.The following understanding are obtained based on model and real data tests:①The three-parameter wavelet has high flexibility,the modulation frequencyσof the wavelet affects the vibration degree of the wavelet,the energy attenuation parameterτcontrols attenuation speed of attenuation function.When the value ofτis relatively big,theσhas less influence on the wavelet.The influence of the energy lag parameterβon the wavelet shape is more complex.Whenβis an integer multiple of trigonometric function period,only wavelet time shift occurs,it can be used to match the zero phase wavelet;when theβis not an integer multiple of trigonometric function period,wavelet time shift and deformation are generated,it can be used to match the non-zero phase wavelet.Deeply buried channels may be misinterpreted whenβis not equal to zero;②Compared with Morlet wavelet,three-parameter wavelet can more precisely depict subtle sedimentary structures in thin interbeds;③In real data processing,the optimal parameter combinations can be obtained according to the correlation of various forms of basic wavelets with real wavelet extracted from targets.
We first describe the basic principle of spec-trum decomposition,then introduce three-parameter wavelet,study the influence of each parameter on wavelet.After that we conduct time-frequency analysis with three parameters based on forward modeling.Finally we perform reservoir characterization with three-parameter wavelet.It is found that subtle geological information can be highlighted.The following understanding are obtained based on model and real data tests:①The three-parameter wavelet has high flexibility,the modulation frequencyσof the wavelet affects the vibration degree of the wavelet,the energy attenuation parameterτcontrols attenuation speed of attenuation function.When the value ofτis relatively big,theσhas less influence on the wavelet.The influence of the energy lag parameterβon the wavelet shape is more complex.Whenβis an integer multiple of trigonometric function period,only wavelet time shift occurs,it can be used to match the zero phase wavelet;when theβis not an integer multiple of trigonometric function period,wavelet time shift and deformation are generated,it can be used to match the non-zero phase wavelet.Deeply buried channels may be misinterpreted whenβis not equal to zero;②Compared with Morlet wavelet,three-parameter wavelet can more precisely depict subtle sedimentary structures in thin interbeds;③In real data processing,the optimal parameter combinations can be obtained according to the correlation of various forms of basic wavelets with real wavelet extracted from targets.
引文
[1] Widess M B.How thin is a thin bed?.Geophysics,1973,38(6):1176-1180.
[2] Neidell N S,Poggiagliolmi E.Seismic Stratigraphy-Applications to Hydrocarbon Exploration:AAPG Special Volumes,1977:389-416.
[3] Partyka G.Interpretational applications of spectral decomposition in reservoir characterization.The Leading Edge,1999,18(3):173-184.
[4] 刘峰,牟中海,蒋裕强等.小断层的地震识别.地球物理学进展,2011,26(6):2210-2215.Liu Feng,Mou Zhonghai,Jiang Yuqiang et al.Seismic identification of small fault.Progress in Geophysics,2011,26(6):2210-2215.
[5] 李伟,岳大力,胡光义等.分频段地震属性优选及砂体预测方法——秦皇岛32-6油田北区实例.石油地球物理勘探,2017,52(1):121-130.Li Wei,Yue Dali,Hu Guangyi et al.Frequency-segmented seismic attribute optimization and sandbody distribution prediction:An example in North Block,Qinghuangdao 32-6 Oilfield.OGP,2017,52(1):121-130.
[6] 于豪,李劲松,张研等.频谱分解技术在断层与储层识别中的应用.石油地球物理勘探,2013,48(6):954-959.Yu Hao,Li Jinsong,Zhang Yan et al.Spectral decomposition in fault and reservoir identifications.OGP,2013,48(6):954-959.
[7] 朱振宇,吕丁友,桑淑云等.基于物理小波的频谱分解方法及应用研究.地球物理学报,2009,52(8):2152-2157.Zhu Zhenyu,Lü Dingyou,Sang Shuyun et al.Research of spectrum decomposition method based on physical wavelet transform and its application.Chinese Journal of Geophysics,2009,52(8):2152-2157.
[8] 李斌,乐友喜,温明明.同步挤压小波变换在储层预测中的应用.天然气地球科学,2017,28(2):341-348.Li Bin,Yue Youxi,Wen Mingming.Reservoir predication based on synchrosqueezing wavelet transform.Natural Gas Geoscience,2017,28(2):341-348.
[9] 刘顺兰,钱惠生,徐平原.Morlet小波变换在检测瞬时信号中的应用.杭州电子工业学院学报,1999,19(3):29-36.Liu Shunlan,Qian Huisheng,Xu Pingyuan.Application of the Morlet wavelet transform to detection of transient signals.Journal of Hangzhou Institute of Electronic Engineering,1999,19 (3):29-36.
[10] Harrop J D,Taraskin S N,Elliott S R.Instantaneous frequency and amplitude identification using wavelets:application to glass structure.Statistical Nonlinear & Soft Matter Physics,2002,doi:10.1103/PhysRevE.66.026703.
[11] 高静怀,万涛,陈文超等.三参数小波及其在地震资料分析中的应用.地球物理学报,2006,49(6):1802-1812.Gao Jinghuai,Wan Tao,Chen Wenchao et al.Three parameter wavelet and its application in seismic data analysis.Chinese Journal of Geophysics,2006,49(6):1802-1812.
[12] 冯斌,赵峰华,王淑华.地震分频解释技术在河道砂预测中的应用.地球科学进展,2012,27(5):510-514.Feng Bin,Zhao Fenghua,Wang Shuhua.Application of spectral decomposition technique in fluvial sand body prediction.Advances in Earth Science,2012,27(5):510-514.
[13] Marfurt K J,Kirlin R L.Narrow-band spectral analysis and thin-bed tuning.Geophysics,2001,66(4):1274-1283.
[14] 马跃华,吴蜀燕,白玉花等.利用谱分解技术预测河流相储层.石油地球物理勘探,2015,50(3):502-509.Ma Yuehua,Wu Shuyan,Bai Yuhua et al.River sedimentary microfacies prediction based on spectral decomposition.OGP,2015,50(3):502-509.
[15] 朱秋影,魏国齐,杨威等.利用时频分析技术预测依拉克构造有利砂体分布.石油地球物理勘探,2017,52(3):538-547.Zhu Qiuying,Wei Guoqi,Yang Wei et al.Favorable sand body prediction based on the time-frequency analysis in Iraqi Structure.OGP,2017,52(3):538-547.
[16] 马德波,贾进华,申银民等.塔里木盆地吉拉克地区东河砂岩尖灭线地震预测技术与应用.石油地球物理勘探,2017,52(1):94-104.Ma Debo,Jia Jinhua,Shen Yinmin et al.Seismic pre-diction of Donghe sandstone pinch-out line in Jilake,Tarim Basin.OGP,2017,52(1):94-104.
[17] 陈雨红,杨长春,曹齐放等.几种时频分析方法比较.地球物理学进展,2006,21(4):1180-1185.Chen Yuhong,Yang Changchun,Cao Qifang et al.The comparison of some time-frequency analysis methods.Progress in Geophysics,2006,21(4):1180-1185.
[18] Grossmann A,Morlet J.Decomposition of Hardy functions into square integrable wavelets of constant shape.Siam Journal on Mathematical Analysis,1984,15(4):723-736
[19] 黄捍东,张如伟,郭迎春.地震信号的小波分频处理.石油天然气学报,2008,30(3):87-91.Huang Handong,Zhang Ruwei,Guo Yingchun.Wavelet frequency-division process for seismic signals.Journal of Oil & Gas Technology,2008,30(3):87-91.
[20] 高静怀,汪文秉,朱光明等.地震资料处理中小波函数的选取研究.地球物理学报,1996,39(3):392-400.Gao Jinghuai,Wang Wenbing,Zhu Guangming et al.On the choice of wavelet functions for seismic data processing.Chinese Journal of Geophysics,1996,39(3):392-400.
[21] 熊晓军,王飞,米鸿等.基于时频三参数小波变换的频率衰减梯度分析.石油地球物理勘探,2015,50(4):699-705.Xiong Xiaojun,Wang Fei,Mi Hong et al.Frequency attenuation analysis based on time-frequency three-parameter wavelet transform.OGP,2015,50(4):699-705.
[22] 姜秀娣,翁斌,刘亚茹等.分频混色技术在高精度地震解释中的应用.地球物理学进展,2013,28(2):882-888.Jiang Xiudi,Weng Bin,Liu Yaru et al.Application of spectral decomposition RGB plotting technique for spectral components in high accuracy seismic interpretation.Progress in Geophysics,2013,28(2):882-888.
[2] Neidell N S,Poggiagliolmi E.Seismic Stratigraphy-Applications to Hydrocarbon Exploration:AAPG Special Volumes,1977:389-416.
[3] Partyka G.Interpretational applications of spectral decomposition in reservoir characterization.The Leading Edge,1999,18(3):173-184.
[4] 刘峰,牟中海,蒋裕强等.小断层的地震识别.地球物理学进展,2011,26(6):2210-2215.Liu Feng,Mou Zhonghai,Jiang Yuqiang et al.Seismic identification of small fault.Progress in Geophysics,2011,26(6):2210-2215.
[5] 李伟,岳大力,胡光义等.分频段地震属性优选及砂体预测方法——秦皇岛32-6油田北区实例.石油地球物理勘探,2017,52(1):121-130.Li Wei,Yue Dali,Hu Guangyi et al.Frequency-segmented seismic attribute optimization and sandbody distribution prediction:An example in North Block,Qinghuangdao 32-6 Oilfield.OGP,2017,52(1):121-130.
[6] 于豪,李劲松,张研等.频谱分解技术在断层与储层识别中的应用.石油地球物理勘探,2013,48(6):954-959.Yu Hao,Li Jinsong,Zhang Yan et al.Spectral decomposition in fault and reservoir identifications.OGP,2013,48(6):954-959.
[7] 朱振宇,吕丁友,桑淑云等.基于物理小波的频谱分解方法及应用研究.地球物理学报,2009,52(8):2152-2157.Zhu Zhenyu,Lü Dingyou,Sang Shuyun et al.Research of spectrum decomposition method based on physical wavelet transform and its application.Chinese Journal of Geophysics,2009,52(8):2152-2157.
[8] 李斌,乐友喜,温明明.同步挤压小波变换在储层预测中的应用.天然气地球科学,2017,28(2):341-348.Li Bin,Yue Youxi,Wen Mingming.Reservoir predication based on synchrosqueezing wavelet transform.Natural Gas Geoscience,2017,28(2):341-348.
[9] 刘顺兰,钱惠生,徐平原.Morlet小波变换在检测瞬时信号中的应用.杭州电子工业学院学报,1999,19(3):29-36.Liu Shunlan,Qian Huisheng,Xu Pingyuan.Application of the Morlet wavelet transform to detection of transient signals.Journal of Hangzhou Institute of Electronic Engineering,1999,19 (3):29-36.
[10] Harrop J D,Taraskin S N,Elliott S R.Instantaneous frequency and amplitude identification using wavelets:application to glass structure.Statistical Nonlinear & Soft Matter Physics,2002,doi:10.1103/PhysRevE.66.026703.
[11] 高静怀,万涛,陈文超等.三参数小波及其在地震资料分析中的应用.地球物理学报,2006,49(6):1802-1812.Gao Jinghuai,Wan Tao,Chen Wenchao et al.Three parameter wavelet and its application in seismic data analysis.Chinese Journal of Geophysics,2006,49(6):1802-1812.
[12] 冯斌,赵峰华,王淑华.地震分频解释技术在河道砂预测中的应用.地球科学进展,2012,27(5):510-514.Feng Bin,Zhao Fenghua,Wang Shuhua.Application of spectral decomposition technique in fluvial sand body prediction.Advances in Earth Science,2012,27(5):510-514.
[13] Marfurt K J,Kirlin R L.Narrow-band spectral analysis and thin-bed tuning.Geophysics,2001,66(4):1274-1283.
[14] 马跃华,吴蜀燕,白玉花等.利用谱分解技术预测河流相储层.石油地球物理勘探,2015,50(3):502-509.Ma Yuehua,Wu Shuyan,Bai Yuhua et al.River sedimentary microfacies prediction based on spectral decomposition.OGP,2015,50(3):502-509.
[15] 朱秋影,魏国齐,杨威等.利用时频分析技术预测依拉克构造有利砂体分布.石油地球物理勘探,2017,52(3):538-547.Zhu Qiuying,Wei Guoqi,Yang Wei et al.Favorable sand body prediction based on the time-frequency analysis in Iraqi Structure.OGP,2017,52(3):538-547.
[16] 马德波,贾进华,申银民等.塔里木盆地吉拉克地区东河砂岩尖灭线地震预测技术与应用.石油地球物理勘探,2017,52(1):94-104.Ma Debo,Jia Jinhua,Shen Yinmin et al.Seismic pre-diction of Donghe sandstone pinch-out line in Jilake,Tarim Basin.OGP,2017,52(1):94-104.
[17] 陈雨红,杨长春,曹齐放等.几种时频分析方法比较.地球物理学进展,2006,21(4):1180-1185.Chen Yuhong,Yang Changchun,Cao Qifang et al.The comparison of some time-frequency analysis methods.Progress in Geophysics,2006,21(4):1180-1185.
[18] Grossmann A,Morlet J.Decomposition of Hardy functions into square integrable wavelets of constant shape.Siam Journal on Mathematical Analysis,1984,15(4):723-736
[19] 黄捍东,张如伟,郭迎春.地震信号的小波分频处理.石油天然气学报,2008,30(3):87-91.Huang Handong,Zhang Ruwei,Guo Yingchun.Wavelet frequency-division process for seismic signals.Journal of Oil & Gas Technology,2008,30(3):87-91.
[20] 高静怀,汪文秉,朱光明等.地震资料处理中小波函数的选取研究.地球物理学报,1996,39(3):392-400.Gao Jinghuai,Wang Wenbing,Zhu Guangming et al.On the choice of wavelet functions for seismic data processing.Chinese Journal of Geophysics,1996,39(3):392-400.
[21] 熊晓军,王飞,米鸿等.基于时频三参数小波变换的频率衰减梯度分析.石油地球物理勘探,2015,50(4):699-705.Xiong Xiaojun,Wang Fei,Mi Hong et al.Frequency attenuation analysis based on time-frequency three-parameter wavelet transform.OGP,2015,50(4):699-705.
[22] 姜秀娣,翁斌,刘亚茹等.分频混色技术在高精度地震解释中的应用.地球物理学进展,2013,28(2):882-888.Jiang Xiudi,Weng Bin,Liu Yaru et al.Application of spectral decomposition RGB plotting technique for spectral components in high accuracy seismic interpretation.Progress in Geophysics,2013,28(2):882-888.