沿层地震属性数据空间滤波去噪方法
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摘要
由于受地震采集观测系统的影响和属性数据提取方法的约束,沿层地震属性数据上会出现一些规则的线性噪声和随机噪声,这些噪声对于进一步应用地震属性进行储层预测和含油气检测有严重影响。为了滤除这些噪声,提高属性精度,本文采用因子克里金分析,在变差函数域将变差函数分解为不同变化尺度上的分量,进而在空间域将区域化变量分解为不同角度、不同尺度上的空间分量。这样就可以得到空间各分量对克里金估计结果的贡献。对这些分量进行选择就可以得到有用信息,同时滤除噪声和人为假象,达到滤波的目的。文中在对变差函数进行分解的同时,还推导出因子克里金方程组。经理论数据的试算和对实际地震数据处理,说明此法是可行的。
Since influence of seismic acquisition geometry and constraint of attributes data detection, the horizon-oriented seismic attributes data may appeal some regular linear noises and random noises that seriously affect next application of seismic attributes to carry out reservoir prediction and oil/gas-bearing detection.In order to filter these noises and improve the precision of detected attributes, the paper uses factorial Kriging analysis, decomposes the variation function in variation function domain into components in different variant scales, and then in spatial domain decomposes the regional variables into spatial components in different angles and scales,which can yield the contribution of separate components to Kriging estimation.The selection among these components can yield useful information,and filter noises and artifacts at the same time,reaching the filtering goal.The paper also deduces factor Kriging equation set while decomposing variation function. The approach has been proved to be feasible after the test of theoretical data and practical seismic data processing.
Since influence of seismic acquisition geometry and constraint of attributes data detection, the horizon-oriented seismic attributes data may appeal some regular linear noises and random noises that seriously affect next application of seismic attributes to carry out reservoir prediction and oil/gas-bearing detection.In order to filter these noises and improve the precision of detected attributes, the paper uses factorial Kriging analysis, decomposes the variation function in variation function domain into components in different variant scales, and then in spatial domain decomposes the regional variables into spatial components in different angles and scales,which can yield the contribution of separate components to Kriging estimation.The selection among these components can yield useful information,and filter noises and artifacts at the same time,reaching the filtering goal.The paper also deduces factor Kriging equation set while decomposing variation function. The approach has been proved to be feasible after the test of theoretical data and practical seismic data processing.
引文
[1]Mundi m E Cand Johann Paulo.Factorial kriging anal-ysis:Geostatistical filtering applied to reservoir char-acterization.The Leading Edge,1999
[2]Coleou T.Ti me-lapse filtering and i mproved repeat-ability with automatic factorial co-kriging(AFACK).EAGE64th Conference&Exhibition,Florence,Italy,2002,27~30
[3]Yarus Jeffrey.Isatis offers feature extraction via Facto-rial Kriging.QGSI,Houston,USA,2003
[4]王家华,高海余,周叶.克里金地质绘图技术——计算机的模型和算法.北京:石油工业出版社,1999
[5]吴胜和,金振奎,黄沧钿,陈崇河.储层建模.北京:石油工业出版社,1999
[6]牛文杰,杨钦.超大数据体泛克里金插值的研究.石油地球物理勘探,2001,36(6):672~679
[7]杨慧珠,巴晶,唐建侯,聂建新,卢旺辉.油气勘探中常规地球物理方法的发展.石油地球物理勘探,2006,41(2):231~236
[2]Coleou T.Ti me-lapse filtering and i mproved repeat-ability with automatic factorial co-kriging(AFACK).EAGE64th Conference&Exhibition,Florence,Italy,2002,27~30
[3]Yarus Jeffrey.Isatis offers feature extraction via Facto-rial Kriging.QGSI,Houston,USA,2003
[4]王家华,高海余,周叶.克里金地质绘图技术——计算机的模型和算法.北京:石油工业出版社,1999
[5]吴胜和,金振奎,黄沧钿,陈崇河.储层建模.北京:石油工业出版社,1999
[6]牛文杰,杨钦.超大数据体泛克里金插值的研究.石油地球物理勘探,2001,36(6):672~679
[7]杨慧珠,巴晶,唐建侯,聂建新,卢旺辉.油气勘探中常规地球物理方法的发展.石油地球物理勘探,2006,41(2):231~236
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