基于地震奇异性属性划分砂砾岩扇体沉积界面
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摘要
利用地震数据划分沉积旋回主要是解决二个问题:沉积界面和沉积旋回体,而沉积界面是基础。地震剖面携带着大量的奇异性信息,是地下地质体的沉积、旋回性、波阻抗等综合反映。小波变换具有良好的时频局部化性质,通过Lipschitz指数刻画信号中奇点位置和大小,能够检测地震信号局部奇异性。利用小波变换系数的模极大值法提取地震奇异指数,从而揭示沉积旋回性和沉积界面。这里基于偏移地震数据的小波变换,提取地震奇异性属性,来解决断陷湖盆陡坡带砂砾岩扇体沉积界面的检测。通过实际资料处理,地震奇异性属性成功刻画了砂砾岩扇体的外部形态,同时还可以清晰地呈现出扇内沉积界面与沉积的期次,为进一步的精细解释以及扇体内的油气藏预测提供了有力保障。
There are two main issues to deal with in dividing the sedimentary cycle by seismic data,that is,dividing sedimentary interface and sedimentary cycle body,and sedimentary interface is the foundation of the two issues.Seismic data contains a lot of information of subsurface which is a comprehensive response of subsurface geology including sedimentary,sedimentary cycle,lithology and wave impedance,which could be related to the singularity of seismic signal.Wavelet transform has the characteristics of localization of time frequency,and the singularity location and value can be detected by Lipschitz power which can also be used to detect the singularity of seismic data for dividing the depositional interface and sedimentary cycle.In this paper we try to use wavelet transform to extract the singularity attribute from seismic data,divide the stratum gyration and detect the deposition boundary of sand-conglomerate fan-bodies in the steep-slope belt of continental fault basin.We've tested this method by applying it to a real project,where the exterior geometrical shape and inner depositional texture are clearly identified.Therefore,this method will benefit the further fine interpretation of the glutenite fan and the prediction of the seismic reservoir.
There are two main issues to deal with in dividing the sedimentary cycle by seismic data,that is,dividing sedimentary interface and sedimentary cycle body,and sedimentary interface is the foundation of the two issues.Seismic data contains a lot of information of subsurface which is a comprehensive response of subsurface geology including sedimentary,sedimentary cycle,lithology and wave impedance,which could be related to the singularity of seismic signal.Wavelet transform has the characteristics of localization of time frequency,and the singularity location and value can be detected by Lipschitz power which can also be used to detect the singularity of seismic data for dividing the depositional interface and sedimentary cycle.In this paper we try to use wavelet transform to extract the singularity attribute from seismic data,divide the stratum gyration and detect the deposition boundary of sand-conglomerate fan-bodies in the steep-slope belt of continental fault basin.We've tested this method by applying it to a real project,where the exterior geometrical shape and inner depositional texture are clearly identified.Therefore,this method will benefit the further fine interpretation of the glutenite fan and the prediction of the seismic reservoir.
引文
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[12]STEPHANE MALLAT,HWANG WEN LIANG.Singu-larity detection and processing with wavelets[J].IEEETransactions on Information Theory.1992,38:617.
[13]ARNEODO A,BACRY E,JAFFARD S,and MUZY JF.Singularity spectrum of multifractal functions involvingoscillating singularities[M].J.Fourier Anal.1998.
[2]周静毅,印兴耀,吴国忱.小波变换与地震奇异性属性[J].物探化探计算技术,2006,28(2):117.
[3]BpouoB J IЮ.层系结构解释中地震资料时间谱的分析方法[J].李乐天,译.国外油气勘探,1990,2(1):56.
[4]吴国忱,印兴耀,张广智,等.小波变换在提高地震分辨率及其在地震层序分析中的应用[J].石油物探(增刊),1997,36(增刊):45.
[5]吴国忱,康仁华.三维时频分析方法在地震层序分析中的应用[J].石油大学学报(自然科学版),2000,24(1):81.
[6]王玉梅,季玉新,李东波,等.应用地震属性技术预测A地区储层[J].石油物探,2001,40(4):69.
[7]COHEN L.Time-frequency distributions-a review[J].Proceeding of the IEEE,1989,77(7):941.
[8]DAUBECHIES I.The wavelet transform:time-frequencylocation and signal analysis[J].IEEE Trans.on Infor-mation Theory.1990,36(5):961.
[9]MUSHIN J A,MAKAROV V V.Structural-formationalinterpretation tools for seismic strtigraphy[J].Geophys-ical Prospecting,2000,48:953.
[10]MUSHIN J A.Structure-formation interpretation of seis-mic data[M].Nedro,Moscow(Russian),1990.
[11]MALLET S.A Theory for Multi-resolution signal decom-position.The Wavelet Representation[J].IEEE Trans.on Pattern Analysis and Machine,1989,11(7):674.
[12]STEPHANE MALLAT,HWANG WEN LIANG.Singu-larity detection and processing with wavelets[J].IEEETransactions on Information Theory.1992,38:617.
[13]ARNEODO A,BACRY E,JAFFARD S,and MUZY JF.Singularity spectrum of multifractal functions involvingoscillating singularities[M].J.Fourier Anal.1998.
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