基于同步挤压改进短时傅里叶变换的谱分解应用
|
摘要
随着油气勘探开发深度的增加以及地震数据采集受外界的干扰严重,使得地震资料处理解释人员对于含油气层的识别也变得更加困难。基于时频分析的地震谱分解技术已经广泛应用于油气储层预测中;但由于短时傅里叶变换、小波变换、S变换、Wigner-Ville分布等传统时频分析方法受自身窗函数的约束,使得它们的时频聚焦性不高或交叉项干扰,导致油气检测结果存在很大的误差。针对这一难题,为了实现准确的储层预测,通过对短时傅里叶窗函数进行拓展,并且对拓展后的短时傅里叶变换结果执行挤压,将挤压结果重排放置于信号的瞬时频率处,提出了同步挤压改进短时傅里叶变换。信号分析表明同步挤压改进短时傅里叶变换具有更高的时频聚焦能力。将同步挤压改进短时傅里叶变换与地震谱分解技术结合,并将其运用于实际地震资料,结果表明,该方法可以对含油气层进行精细刻画,频率异常特征十分显著,对于含油气性检测具有很强的实用性。
With the rise of the depth of the oil and gas exploration and development, and due to the severe disturbance of the seismic data acquisition by the outside world, it is more difficult for the seismic data processing and interpreting personnel to identify the oil and gas layers. The seismic spectrum decomposing technique based on the time-frequency analysis has been widely applied in the prediction of the oil and gas reservoirs; however, because the traditional time-frequency analysis methods such as short-time Fourier transform, wavelet transform, S-transform, Wigner-Ville distribution and so on are constrained by their own window functions, their time-frequency focusing property is not high or the cross-terms are interfered, those result in a large error in the oil and gas detection results. For this problem, in order to achieve the accurate reservoir prediction, the short-time Fourier window function was expanded, and moreover the expanded short-time Fourier transform results were extruded, the extrusion result was rearranged at the instantaneous frequency of the signal, and furthermore the short-time improved Fourier transform by the synchronous extrusion was developed. The signal analysis shows that the improved short-time Fourier transform has pretty higher time-frequency focusing ability. The new transform was integrated with the seismic spectrum decomposing technique and then applied in the actual seismic data. The results show that the method can carry out the fine characterization of the oil-bearing layer, the frequency anomaly is very obvious, thus it has much stronger practicability for the oil and gas detection.
With the rise of the depth of the oil and gas exploration and development, and due to the severe disturbance of the seismic data acquisition by the outside world, it is more difficult for the seismic data processing and interpreting personnel to identify the oil and gas layers. The seismic spectrum decomposing technique based on the time-frequency analysis has been widely applied in the prediction of the oil and gas reservoirs; however, because the traditional time-frequency analysis methods such as short-time Fourier transform, wavelet transform, S-transform, Wigner-Ville distribution and so on are constrained by their own window functions, their time-frequency focusing property is not high or the cross-terms are interfered, those result in a large error in the oil and gas detection results. For this problem, in order to achieve the accurate reservoir prediction, the short-time Fourier window function was expanded, and moreover the expanded short-time Fourier transform results were extruded, the extrusion result was rearranged at the instantaneous frequency of the signal, and furthermore the short-time improved Fourier transform by the synchronous extrusion was developed. The signal analysis shows that the improved short-time Fourier transform has pretty higher time-frequency focusing ability. The new transform was integrated with the seismic spectrum decomposing technique and then applied in the actual seismic data. The results show that the method can carry out the fine characterization of the oil-bearing layer, the frequency anomaly is very obvious, thus it has much stronger practicability for the oil and gas detection.
引文
[1]MARGRAVE G F.Gabor deconvolution of seismic data for source waveform and Q correction[J].Society of Exploration Geophysicists,2002,79(12):2190-2193.
[2]NEWLAND D E.Harmonic wavelet analysis.[J].Proceings of the Royal Society of London A,1917,443(10):203-225.
[3]STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:The S transform[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[4]陈学华,贺振华,钟文丽.低频阴影与储层特征关系的数值模拟[J].中国矿业大学学报,2011,40(4):584-591.CHEN Xuehua,HE Zhenhua,ZHONG Wenli.Numerical simulation of the relationship between low frequency shadow and reservoir characteristics[J].Journal of China University of Mining & Technology,2011,40(4):584-591.
[5]陈学华,贺振华,黄德济,等.时频域油气储层低频阴影检测[J].地球物理学报,2009,52(1):215-221.CHEN Xuehua,HE Zhenhua,HUANG Deji,et al.Low frequency shadow detection of gas reservoirs in time-frequency domain[J].Chinese Journal of Geophysics,2009,52(1):215-221.
[6]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].Proceedings of the Royal Society of London A,1998,454(1971):903-995.
[7]PENG Z K,PETER W T,CHU F L.A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J].Mechanical Systems and Signal Processing,2005,19(5):974-988.
[8]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.
[9]YU Gang,YU Mingjin,XU Chuanyan.Synchroextracting transform[J].IEEE Transactions on Industrial Electronics,2017,64(10):8042-8054.
[10]牛聪,刘志斌.基于谱分析的低频阴影检测技术[J].海洋石油,2008,28(2):58-61,67.NIU Cong,LIU Zhibin.Low frequency shadow detection technology based on spectral analysis[J].Offshore Oil,2008,28(2):58-61,67.
[11]何胜.广义S变换在薄储层识别中的研究与应用[J].中国石油和化工标准与质量,2017,37(1):38-39.HE Sheng.Research and application of generalized S transform in thin reservoir identification[J].China Petroleum and Chemical Industry Standards and Quality,2017,37(1):38-39.
[12]王元君,周怀来.时频域动态反褶积方法研究[J].西南石油大学学报(自然科学版),2015,37(1):1-10.WANG Yuanjun,ZHOU Huailai.Research on dynamic deconvolution method in time-frequency domain[J].Journal of Southwest Petroleum University (Natural Science Edition),2015,37(1):1-10.
[13]何鹏,周钰邦,朱帅润.基于广义S变换的吸收衰减参数提取[J].科技创新与应用,2018,8(3):31-32.HE Peng,ZHOU Yubang,ZHU Shuairun.Extraction of absorption attenuation parameters based on generalized S transform[J].Science and Technology Innovation and Application,2018,8(3):31-32.
[14] 刘晗,张建中,黄忠来.基于同步挤压S变换的地震信号时频分析[J].石油地球物理勘探,2017,52(4):689-695,623.LIU Han,ZHANG Jianzhong,HUANG Zhonglai.Time-frequency analysis of seismic signals based on synchronous extrusion S transform[J].Petroleum Geophysical Exploration,2017,52(4):689-695,623.
[15]黄忠来,张建中.同步挤压S变换[J].中国科学:信息科学,2016,46(5):643-650.HUANG Zhonglai,ZHANG Jianzhong.Synchronous squeezing S transform[J].Chinese Science:Information Science,2016,46(5):643-650.
[16]李斌.同步挤压小波变换及其在薄互层中的应用[D].青岛:中国石油大学(华东),2015.LI Bin.Synchronous squeeze wavelet transform and its application in thin interbed[D].Qingdao:China University of Petroleum (East China),2015.
[17]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[J].天然气地球科学,2017 ,28(2) :341-348.LI Bin,YUE Youxi,WEN Mingming.Application of synchronous extrusion wavelet transform in reservoir prediction[J].Natural Gas Geoscience,2017,28(2) :341-348.
[18]严海滔,黄饶,周怀来,等.同步挤压广义S变换在南海油气识别中的应用[J/OL].地球物理学进展,2019,34(1):1-16[2018-12-14].http://kns.cnki.net/kcms/detail/11.2982.P.20181022.1601.096.html.YAN Haitao,HUANG Rao,ZHOU Huailai,et al.Application of synchronous squeezing generalized S transform in oil and gas identification in the South China Sea [J/OL].Progress in Geophysics,2019,34(1):1-16[2018-12-14].http://kns.cnki.net/kcms/detail/11.2982.P.20181022.1601.096.html.
[2]NEWLAND D E.Harmonic wavelet analysis.[J].Proceings of the Royal Society of London A,1917,443(10):203-225.
[3]STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:The S transform[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[4]陈学华,贺振华,钟文丽.低频阴影与储层特征关系的数值模拟[J].中国矿业大学学报,2011,40(4):584-591.CHEN Xuehua,HE Zhenhua,ZHONG Wenli.Numerical simulation of the relationship between low frequency shadow and reservoir characteristics[J].Journal of China University of Mining & Technology,2011,40(4):584-591.
[5]陈学华,贺振华,黄德济,等.时频域油气储层低频阴影检测[J].地球物理学报,2009,52(1):215-221.CHEN Xuehua,HE Zhenhua,HUANG Deji,et al.Low frequency shadow detection of gas reservoirs in time-frequency domain[J].Chinese Journal of Geophysics,2009,52(1):215-221.
[6]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].Proceedings of the Royal Society of London A,1998,454(1971):903-995.
[7]PENG Z K,PETER W T,CHU F L.A comparison study of improved Hilbert-Huang transform and wavelet transform:Application to fault diagnosis for rolling bearing[J].Mechanical Systems and Signal Processing,2005,19(5):974-988.
[8]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.
[9]YU Gang,YU Mingjin,XU Chuanyan.Synchroextracting transform[J].IEEE Transactions on Industrial Electronics,2017,64(10):8042-8054.
[10]牛聪,刘志斌.基于谱分析的低频阴影检测技术[J].海洋石油,2008,28(2):58-61,67.NIU Cong,LIU Zhibin.Low frequency shadow detection technology based on spectral analysis[J].Offshore Oil,2008,28(2):58-61,67.
[11]何胜.广义S变换在薄储层识别中的研究与应用[J].中国石油和化工标准与质量,2017,37(1):38-39.HE Sheng.Research and application of generalized S transform in thin reservoir identification[J].China Petroleum and Chemical Industry Standards and Quality,2017,37(1):38-39.
[12]王元君,周怀来.时频域动态反褶积方法研究[J].西南石油大学学报(自然科学版),2015,37(1):1-10.WANG Yuanjun,ZHOU Huailai.Research on dynamic deconvolution method in time-frequency domain[J].Journal of Southwest Petroleum University (Natural Science Edition),2015,37(1):1-10.
[13]何鹏,周钰邦,朱帅润.基于广义S变换的吸收衰减参数提取[J].科技创新与应用,2018,8(3):31-32.HE Peng,ZHOU Yubang,ZHU Shuairun.Extraction of absorption attenuation parameters based on generalized S transform[J].Science and Technology Innovation and Application,2018,8(3):31-32.
[14] 刘晗,张建中,黄忠来.基于同步挤压S变换的地震信号时频分析[J].石油地球物理勘探,2017,52(4):689-695,623.LIU Han,ZHANG Jianzhong,HUANG Zhonglai.Time-frequency analysis of seismic signals based on synchronous extrusion S transform[J].Petroleum Geophysical Exploration,2017,52(4):689-695,623.
[15]黄忠来,张建中.同步挤压S变换[J].中国科学:信息科学,2016,46(5):643-650.HUANG Zhonglai,ZHANG Jianzhong.Synchronous squeezing S transform[J].Chinese Science:Information Science,2016,46(5):643-650.
[16]李斌.同步挤压小波变换及其在薄互层中的应用[D].青岛:中国石油大学(华东),2015.LI Bin.Synchronous squeeze wavelet transform and its application in thin interbed[D].Qingdao:China University of Petroleum (East China),2015.
[17]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[J].天然气地球科学,2017 ,28(2) :341-348.LI Bin,YUE Youxi,WEN Mingming.Application of synchronous extrusion wavelet transform in reservoir prediction[J].Natural Gas Geoscience,2017,28(2) :341-348.
[18]严海滔,黄饶,周怀来,等.同步挤压广义S变换在南海油气识别中的应用[J/OL].地球物理学进展,2019,34(1):1-16[2018-12-14].http://kns.cnki.net/kcms/detail/11.2982.P.20181022.1601.096.html.YAN Haitao,HUANG Rao,ZHOU Huailai,et al.Application of synchronous squeezing generalized S transform in oil and gas identification in the South China Sea [J/OL].Progress in Geophysics,2019,34(1):1-16[2018-12-14].http://kns.cnki.net/kcms/detail/11.2982.P.20181022.1601.096.html.