储集层特征的多尺度差异分析
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摘要
在岩性油气藏勘探中,真、假亮点识别是决定勘探成败的关键。AVO理论,为利用地震资料的叠前信息进行储集层识别提供了理论依据。而频率随偏移距而变化(FVO)这一事实,使得利用地震记录的叠后信息进行储集层预测成为可能,尽管其物理机制目前还不是十分清晰。数值实验表明,叠加地震记录不同尺度地震波同相轴走向的差异与反射能量的差异分别反映了地层岩性的空间变化和区分了地震波调谐产生的强振幅与储集层AVO产生的强振幅,从而为叠后剖面真、假亮点的研究提供了一种新的有效判别方法。MarmousiⅡ模型和实际资料的处理结果表明,本方法可以较好地识别储集层。
The recognition of true and false bright spots is the key of success of lithologic oil-gas reservoir exploration. AVO (amplitude versus offset) theory provides theoretical basis for application of pre-stack information of seismic data to reservoir recognition. FVO (frequency versus offset) allows the prediction of reservoir using the post-stack information to be possible, despite its physical mechanism is now unclear. The numerical experiment indicates that the space variation of formation lithology can be reflected by the strike differences of different scales of seismic wave events in stack seismic record, and the strong amplitude caused by seismic wave tuning can be separated from that caused by reservoir's AVO applying the differences of multiple scales of seismic wave event reflectance energy. These provide a new method for effective recognition or distinguishing of reservoir for study of true and false bright spots in post-stack profile. Marmousi II model and real data processing show that this method can be used to recognize reservoir rocks.
The recognition of true and false bright spots is the key of success of lithologic oil-gas reservoir exploration. AVO (amplitude versus offset) theory provides theoretical basis for application of pre-stack information of seismic data to reservoir recognition. FVO (frequency versus offset) allows the prediction of reservoir using the post-stack information to be possible, despite its physical mechanism is now unclear. The numerical experiment indicates that the space variation of formation lithology can be reflected by the strike differences of different scales of seismic wave events in stack seismic record, and the strong amplitude caused by seismic wave tuning can be separated from that caused by reservoir's AVO applying the differences of multiple scales of seismic wave event reflectance energy. These provide a new method for effective recognition or distinguishing of reservoir for study of true and false bright spots in post-stack profile. Marmousi II model and real data processing show that this method can be used to recognize reservoir rocks.
引文
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[2]Castagna J P.An introduction to this special section:AVO—the next step[J].The Leading Edge,2000,19:1187.
[3]Hilterman.Distinguished Lecture[J].SEG,2001.
[4]Chopra S,Marfurt K J.Seismic attributes for prospect identi-fication and reservoir characterization[J].SEG,2007.
[5]Partyka G R.Distinguished Lecture[J].SEG,2005.
[6]Castagna J P,Sun S,Seigfried R W.Instantaneous spectral analysis:detection of low-frequency shadows associated withhydrocarbon[sJ].The Leading Edge,2003,22:120-127.
[7]Korneev V A,Goloshubin G M,Daley T M,et al.Seismic low-frequency effects in monitoring fluidsaturated reservoirs[J].Geophysics,2004,69:522-532.
[8]Rapoport M B,Rapoport L I,Ryjkov V I.Direct detection of oil and gas fields based on seismic inelasticity effec[tJ].The Leading Edge,2004,23:276-278.
[9]Feng Shen.Azimuthal offset-dependent attributes applied to fracture detection in a carbonate reservoir[J].Geophysics,2002,67(2):365-378.
[10]Batzle M,Hofmann R,Han D,et al.Fluids and frequency dependent seismic velocity of rocks[J].The Leading Edge,2001,20:168-171.
[11]Castagna J P,Batzle M L,Kan T K.Rock physics—the link between rock properties and AVO response:offset depen-dent reflectivity[J].Theory and Practice of AVO Anal-ysis,1993,8:135-171.
[12]Shuey R T.A simplification of the Zoeppritz equations[J].Geophysics,1985,50:609-614.
[13]Grossmann A,Morlet J.Decomposition of Hardy functions into square integrable wavelets of constant shape[J].SIAM Journal on Mathematical Analysis,1984,15:723-736.
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