基于多地震属性的高分辨率拟测井参数的预测
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摘要
基于多地震属性、测井资料,用多变量回归算法成功地对胜利油田 ken-71地区的拟测井参数做出了预测.计算采用了 Daniel P.Hampson 提出的将多变量回归权重系数推广为具有一定时间长度的褶积算子,使预测结果的分辨率获得提高.对该方法提高分辨率的原理做了详细的讨论,给出了该算法在胜利油田 ken-71地区采用常规地面地震数据和测井数据,预测得到的目标区域的拟孔隙度参数的分布,结果显示使用该方法深度分辨率可达8～10m.
The prediction of high resolution pseudo log based on multiple seismic attributes and well logs is achieved in the Ken-71 area of Shengli Oilfield.In this calculation,the multi- variate regression weight coefficients are extended to convolutional operators with specific time length to improve the depth resolution,which is proposed by Daniel P.Hampson.The princi- ple of enhanced resolution obtained by employing convolutional operator is discussed in detail. The predicted pseudo porosity by using ordinary surface seismic data and well log in this area is presented.The results show that with this approach the 8～10 m depth resolution can be achieved.
The prediction of high resolution pseudo log based on multiple seismic attributes and well logs is achieved in the Ken-71 area of Shengli Oilfield.In this calculation,the multi- variate regression weight coefficients are extended to convolutional operators with specific time length to improve the depth resolution,which is proposed by Daniel P.Hampson.The princi- ple of enhanced resolution obtained by employing convolutional operator is discussed in detail. The predicted pseudo porosity by using ordinary surface seismic data and well log in this area is presented.The results show that with this approach the 8～10 m depth resolution can be achieved.
引文
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4 Shang B Z,Caldwell D H.A bandwidth enhancement workflow through wavelet analysis[J].SEG Technical Program Expanded Abstracts,Society of Exploration Geophysicists,2003,22:2 012-2 015．
5 Chopra S,Alexeev V,Sudhakar V.High frequency restoration of surface seismic data[J].The Leading Edge,2003,22(8) :730-738．
6 Chopra S,Blias E,Manerikar A,et al.Simultaneous acquisition of 3D surface seismic and 3D VSP data-processing and integration[J].SEG Technical Program Expanded Abstracts,Society of Exploration Geophysicists,2002,21:2 337-2 340．
7 Russell B H.The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes[D].Calgary:Department of Geology and Geophysics,Canada,2004．
8 Herrera V M,Russell B,Flores A.Neural networks in reservoir characterization[J].The Leading Edge,2006,25(4) :402-411．
9 Hampson D P,Schuelke J S,Quirein J A.Use of multiattribute transforms to predict log properties from seismic data[J].Geophysics,2001,66(1) :220-236．
2 Brown A R.Interpretation of Three-Dimensional Seismic Data[M].Tulsa:The American Association of Petroleum Geologists and the Society of Expioration Geophysics,1999．
3 Taner T,Luo Y,Kelamis P G,et al.Frequency domain smoothing for enhanced seismic resolution[J].SEG Technical Program Expanded Abstracts,Society of Exploration Geophysicists,2003,22:2 020-2 023．
4 Shang B Z,Caldwell D H.A bandwidth enhancement workflow through wavelet analysis[J].SEG Technical Program Expanded Abstracts,Society of Exploration Geophysicists,2003,22:2 012-2 015．
5 Chopra S,Alexeev V,Sudhakar V.High frequency restoration of surface seismic data[J].The Leading Edge,2003,22(8) :730-738．
6 Chopra S,Blias E,Manerikar A,et al.Simultaneous acquisition of 3D surface seismic and 3D VSP data-processing and integration[J].SEG Technical Program Expanded Abstracts,Society of Exploration Geophysicists,2002,21:2 337-2 340．
7 Russell B H.The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes[D].Calgary:Department of Geology and Geophysics,Canada,2004．
8 Herrera V M,Russell B,Flores A.Neural networks in reservoir characterization[J].The Leading Edge,2006,25(4) :402-411．
9 Hampson D P,Schuelke J S,Quirein J A.Use of multiattribute transforms to predict log properties from seismic data[J].Geophysics,2001,66(1) :220-236．
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