基于同步压缩小波变换的速度频散属性估算方法及应用
|
摘要
随着渤海A构造区油气勘探的不断深入,勘探目标逐渐由构造油气藏转变为构造-岩性复合油气藏。A区构造-岩性油气藏的流体关系复杂,储层非均质性强,导致常规油气检测方法多解性严重,不能为该区油气勘探提供可靠的烃检结果。地震波在含油气储层传播时,不仅会发生较强的能量衰减,同时也会发生较强的速度频散,因此,反映速度频散的属性可作为流体指示因子用于流体检测。基于此,文中提出了一种基于同步压缩小波变换的速度频散属性估算方法,从叠后地震数据中提取出表征速度频散程度的群能量梯度属性,进行油气检测。实际资料处理结果表明:基于同步压缩小波变换的速度频散属性油气检测方法分辨率非常高,对含烃储层具有较好指示效果,降低了油气检测的多解性。
With the development of oil and gas exploration in A structural area of Bohai Sea, the exploration target has gradually changed from the structural reservoir to the structural-lithology composite reservoir. Due to the complicated fluid relationship of structural-lithology composite reservoir and strong reservoir heterogeneity in this area, the multi-solution of conventional hydrocarbon detection methods based on the absorption attribute is serious, which cannot provide reliable hydrocarbon detection results for oil and gas exploration. And their limitation is gradually revealed in practical application. Because the seismic wave propagation through the oil and gas reservoirs would not only produce strong energy attenuation, but also produce strong velocity dispersion. Therefore, the attribute reflecting velocity dispersion can be used as a fluid indicator for fluid detection. Based on this,we study and propose an estimation method of velocity dispersion attribute based on synchrosqueezing wavelet transform(SWT) for hydrocarbon detection. This method extracts the attribute of group energy gradient which represents the velocity dispersion degree from post-stack seismic data to detect oil and gas. The actual data processing results show that the hydrocarbon detection method of velocity dispersion attribute based on SWT has high resolution and good indication for hydrocarbon-bearing reservoir, which effectively reduces the multi-solution of hydrocarbon detection.
With the development of oil and gas exploration in A structural area of Bohai Sea, the exploration target has gradually changed from the structural reservoir to the structural-lithology composite reservoir. Due to the complicated fluid relationship of structural-lithology composite reservoir and strong reservoir heterogeneity in this area, the multi-solution of conventional hydrocarbon detection methods based on the absorption attribute is serious, which cannot provide reliable hydrocarbon detection results for oil and gas exploration. And their limitation is gradually revealed in practical application. Because the seismic wave propagation through the oil and gas reservoirs would not only produce strong energy attenuation, but also produce strong velocity dispersion. Therefore, the attribute reflecting velocity dispersion can be used as a fluid indicator for fluid detection. Based on this,we study and propose an estimation method of velocity dispersion attribute based on synchrosqueezing wavelet transform(SWT) for hydrocarbon detection. This method extracts the attribute of group energy gradient which represents the velocity dispersion degree from post-stack seismic data to detect oil and gas. The actual data processing results show that the hydrocarbon detection method of velocity dispersion attribute based on SWT has high resolution and good indication for hydrocarbon-bearing reservoir, which effectively reduces the multi-solution of hydrocarbon detection.
引文
[1]GOLOSHUBIN G M,KORNEEV V A,VINGALOV V M.Seismic lowfrequency effectsfrom oil-saturated reservoi rzones[M].SaltLake City:Society of Exploration Geophysicists,2002:1813-1816.
[2]CASTAGNA J P,SUN S,SIEGFRIED R W.Instantaneous spe ctral analysis:detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2):120-127.
[3]DAN E.The low-frequency gas shadow on seismic sections[J].The Leading Edge,2004,23(8):772-772.
[4]CHEN G L,FINN C,NEELAMANI R,et al.Spectral decomposition response to reservoir fluids from a deepwater reser voir[M].New Orleans:Society of Exploration Geophysicists,2006:1665-1669.
[5]LIU E,CHAPMAN M,LOIZOU N,et al.Applications of sp ectral decomposition for AVO analyses in the west of Shetland[M].New Orleans:Society of Exploration Geophysicists,2006:279-283.
[6]QUAN Y L,HARRIS J M.Seismic attenuation tomography using the frequency shift method[J].Geophysics,1997,62(3):895-905.
[7]李宏兵,赵文智,曹宏,等.小波尺度域含气储层地震波衰减特[J].地球物理学报,2004,47(5):892-898.
[8]宋红伟,廖林,边树涛,等.白庙气田沙三段地震频谱衰减特征与含气性检测[J].断块油气田,2012,19(2):145-148.
[9]关晓巍.基于双相介质理论的ABV油气检测技术及应用[J].断块油气田,2013,20(4):435-438.
[10]张固澜,贺振华,王熙明,等.地震波频散效应与反Q滤波相位补偿[J].地球物理学报,2014,57(5):1655-1663.
[11]DAUBECHIES I,LU J,WU H T.Synchrosqueezed wavelet transforms:an empirical mode decomposition like tool[J].Applied and Computational Harmonic Analysis,2011,30(2):243-261.
[12]DAU B.Continuous wavelet transform based on auditory nerve models[C]//CRC Press.Wavelets in Medicine and Biology.Boca Raton:CRCPress,1996:527-546.
[13]HERRERA R H,HAN J,MIRKO V D B.Applications of the synchrosqueezing transform in seismic time-frequency analysis[J].Geophysics,2014,79(3):55-64.
[14]甘利灯,裴正林.一种基于速度随频率变化信息的油气检测方法:CN 101738637 A[P].2010-06-16.
[15]蒲明刚.相速、群速和能量传播速度[J].成都信息工程学院学报,1993(4):116-119.
[2]CASTAGNA J P,SUN S,SIEGFRIED R W.Instantaneous spe ctral analysis:detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2):120-127.
[3]DAN E.The low-frequency gas shadow on seismic sections[J].The Leading Edge,2004,23(8):772-772.
[4]CHEN G L,FINN C,NEELAMANI R,et al.Spectral decomposition response to reservoir fluids from a deepwater reser voir[M].New Orleans:Society of Exploration Geophysicists,2006:1665-1669.
[5]LIU E,CHAPMAN M,LOIZOU N,et al.Applications of sp ectral decomposition for AVO analyses in the west of Shetland[M].New Orleans:Society of Exploration Geophysicists,2006:279-283.
[6]QUAN Y L,HARRIS J M.Seismic attenuation tomography using the frequency shift method[J].Geophysics,1997,62(3):895-905.
[7]李宏兵,赵文智,曹宏,等.小波尺度域含气储层地震波衰减特[J].地球物理学报,2004,47(5):892-898.
[8]宋红伟,廖林,边树涛,等.白庙气田沙三段地震频谱衰减特征与含气性检测[J].断块油气田,2012,19(2):145-148.
[9]关晓巍.基于双相介质理论的ABV油气检测技术及应用[J].断块油气田,2013,20(4):435-438.
[10]张固澜,贺振华,王熙明,等.地震波频散效应与反Q滤波相位补偿[J].地球物理学报,2014,57(5):1655-1663.
[11]DAUBECHIES I,LU J,WU H T.Synchrosqueezed wavelet transforms:an empirical mode decomposition like tool[J].Applied and Computational Harmonic Analysis,2011,30(2):243-261.
[12]DAU B.Continuous wavelet transform based on auditory nerve models[C]//CRC Press.Wavelets in Medicine and Biology.Boca Raton:CRCPress,1996:527-546.
[13]HERRERA R H,HAN J,MIRKO V D B.Applications of the synchrosqueezing transform in seismic time-frequency analysis[J].Geophysics,2014,79(3):55-64.
[14]甘利灯,裴正林.一种基于速度随频率变化信息的油气检测方法:CN 101738637 A[P].2010-06-16.
[15]蒲明刚.相速、群速和能量传播速度[J].成都信息工程学院学报,1993(4):116-119.