多子波分解与重构技术应用研究
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摘要
探讨地震信号的多子波分解与重构方法,为地质解释与油气藏描述提供技术支撑。该方法在对地震信号进行分解时,利用匹配追踪算法,将原始地震信号分解成由一系列频率成分不同的Morlet子波函数组成的原子函数库;在重构地震信号时,根据油气响应特征、地质现象描述的需求,先对原子函数库进行筛选、优化,再按照频率属性对Morlet子波函数进行线性求和。在对地震信号进行分解与重构的过程中,实现了将地震信号在时间域内、时间-频率域内进行联合解释,挖掘出了单域内难以发现的地质现象、油气响应特征。将该技术应用到四川新场地区沙溪庙组的储层预测与含气性识别中,获得了与钻井实测结果相吻合的良好效果。
To study the multi-wavelet decomposition and reconstructing technology of seismic data can provide some important supports for predicting the oil-gas reservoir.When the seismic data are decomposed,by the arithmetic of matching pursuit,the wavelet dictionary will be established by a series of Morlet wavelet functions with various frequencies.When the seismic data are reconstructed,according to the requirement of oil-gas response and geology phenomena,the dictionary will be filtered and optimized firstly,and then,the Morlet wavelet functions will be linearly summed by the frequency attribute.The process of decomposing and reconstructing seismic data can combine the time field with the time-frequency field seismic data to interpret and overcome the cognition localization of geology phenomena and oil-gas response character in the single field.The technologies have been used to predict the reservoir and identify the gas and achieved good effects because the predictions are consistent with well loggings.
To study the multi-wavelet decomposition and reconstructing technology of seismic data can provide some important supports for predicting the oil-gas reservoir.When the seismic data are decomposed,by the arithmetic of matching pursuit,the wavelet dictionary will be established by a series of Morlet wavelet functions with various frequencies.When the seismic data are reconstructed,according to the requirement of oil-gas response and geology phenomena,the dictionary will be filtered and optimized firstly,and then,the Morlet wavelet functions will be linearly summed by the frequency attribute.The process of decomposing and reconstructing seismic data can combine the time field with the time-frequency field seismic data to interpret and overcome the cognition localization of geology phenomena and oil-gas response character in the single field.The technologies have been used to predict the reservoir and identify the gas and achieved good effects because the predictions are consistent with well loggings.
引文
[1]Thang N,John C.Matching pursuit of two dimensional seismic data and its filtering application[C]//SEG Ex-panded Abstracts,2000.
[2]Liu J L,Marfurt KJ.Matching pursuit decomposition u-sing Morlet wavelets[C]//SEG/Houston:Annual Meet-ing,2005:786-790.
[3]Wang Y H.Seismic time-frequency spectral decomposi-tion by matching pursuit[J].Geophysics,2007,72(1):13-20.
[4]John P C,Sun S J,Robert W S.Instantaneous spectral analysis:Detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2):120-127.
[5]Satish S,Patrha S R,Phil D A,et al.Spectral decom-position of seismic data with continuous-wavelet transfor-mation[J].Geophysics,2005,70(6):19-25.
[6]高静怀,汪文秉,朱光明,等.地震资料处理中小波函数选取研究[J].地球物理学报,1996,39(3):392-400.
[7]徐天吉,程冰洁.瞬时谱分析技术与气水识别方法研究[J].石油天然气学报,2008,30(4):73-78.
[2]Liu J L,Marfurt KJ.Matching pursuit decomposition u-sing Morlet wavelets[C]//SEG/Houston:Annual Meet-ing,2005:786-790.
[3]Wang Y H.Seismic time-frequency spectral decomposi-tion by matching pursuit[J].Geophysics,2007,72(1):13-20.
[4]John P C,Sun S J,Robert W S.Instantaneous spectral analysis:Detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2):120-127.
[5]Satish S,Patrha S R,Phil D A,et al.Spectral decom-position of seismic data with continuous-wavelet transfor-mation[J].Geophysics,2005,70(6):19-25.
[6]高静怀,汪文秉,朱光明,等.地震资料处理中小波函数选取研究[J].地球物理学报,1996,39(3):392-400.
[7]徐天吉,程冰洁.瞬时谱分析技术与气水识别方法研究[J].石油天然气学报,2008,30(4):73-78.
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