伽马反演在川东T气田储层预测中的应用
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摘要
伽马曲线是反映泥质含量的重要参数,相比速度资料来说更加稳定,不受所含流体的影响。针对含气层来说,利用地震信息进行伽马反演相对于常规的波阻抗反演所描述的砂体展布更为直观。在对目前常用的伽马反演方法进行总结、讨论的基础上,指出地震体属性分析法具有明确的物理意义,并有效结合了多元逐步回归和交互验证法进行属性的优选和组合,采用褶积因子消除地震信息和测井信息的频率差异,以神经网络为拟合和预测手段,加之具有严谨的理论基础,相比其它几种方法更为准确合理。最后,通过川东T气田伽马反演的实例说明该方法的应用,预测了T气田须家河组砂体展布,取得了较好的效果。
The gamma curve is an important parameter indicating the shale content. Compared with the velocity data, it is very stable and won′t be affected by reservoir fluid. For the sandbody distributing description of gas-bearing layers, the gamma inversion using seismic data is more intuitional than the conventional wave impedance inversion. On the basis of summarizing and discussing the gamma inversion, we point out that an analytic method of volume attribute is very important. The method integrates multiple stepwise regression and cross verification to optimize and combine the attribute, adopts the convolution factor to eliminate frequency difference between seismic and logging data, uses the neural network as a matching and predicting tool. Because of its religious theoretic base, the method is more accurate than other ones. At last, through a case study of the gamma inversion in T gasfield of Eastern Sichuan Basin, we predict the sandbody distribution of Xujiahe Formation.
The gamma curve is an important parameter indicating the shale content. Compared with the velocity data, it is very stable and won′t be affected by reservoir fluid. For the sandbody distributing description of gas-bearing layers, the gamma inversion using seismic data is more intuitional than the conventional wave impedance inversion. On the basis of summarizing and discussing the gamma inversion, we point out that an analytic method of volume attribute is very important. The method integrates multiple stepwise regression and cross verification to optimize and combine the attribute, adopts the convolution factor to eliminate frequency difference between seismic and logging data, uses the neural network as a matching and predicting tool. Because of its religious theoretic base, the method is more accurate than other ones. At last, through a case study of the gamma inversion in T gasfield of Eastern Sichuan Basin, we predict the sandbody distribution of Xujiahe Formation.
引文
1唐湘蓉.地震测井联合反演方法研究及应用——以塔河4号油区为例[D].成都理工大学图书馆,2005.
2张永升,李家蓉,潘新志.地震和测井联合反演技术在塔河油田碳酸盐岩储层预测中的应用[J].勘探地球物理进展,2002,25(4):46-48.
3罗权生,吴永富,陈旋,等.模型反演技术在浅层储集层研究中的应用[J].新疆石油地质,2000,21(4):331-332.
4Cooke D,Ball V,Muryanto T.What is the best seismic at-tribute for quantitative seismic reservoir characterization-[A].Annual Meeting Abstracts,Society of Exploration Ge-ophysicists[C],1999:1588-1591.
5Brown A.R.Seismic Attributes and Their Classification[J].The Leading EAEG,1996,15(10):1090.
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7Hampson D P,Schuelke J S,Quirein J A.Use of multiat-tribute transforms to predict Log properties from seismic data[J].Geophysics,2001,66(1):220-236.
8吴大奎,李亚林,伍志明,等.地震、测井资料联合反演的神经网络算法研究[J].天然气工业,2004,24(3):55-57.
9徐明华,王绪本,李学华.自然伽马反演在双家坝构造飞仙关组鲕滩储层预测中的应用[J].石油物探,2003,42(3):346-349.
10Fernando A,Husam M,Patrick M.Pseudo-gamma ray volume from extended elastic impedance inversion for gas ex-ploration[J].The Leading Edge,2004,23:536-540.
2张永升,李家蓉,潘新志.地震和测井联合反演技术在塔河油田碳酸盐岩储层预测中的应用[J].勘探地球物理进展,2002,25(4):46-48.
3罗权生,吴永富,陈旋,等.模型反演技术在浅层储集层研究中的应用[J].新疆石油地质,2000,21(4):331-332.
4Cooke D,Ball V,Muryanto T.What is the best seismic at-tribute for quantitative seismic reservoir characterization-[A].Annual Meeting Abstracts,Society of Exploration Ge-ophysicists[C],1999:1588-1591.
5Brown A.R.Seismic Attributes and Their Classification[J].The Leading EAEG,1996,15(10):1090.
6谢雄举,季玉新.优选地震属性预测储层参数的方法及其应用[J],石油物探,2004,43(增刊):127-131.
7Hampson D P,Schuelke J S,Quirein J A.Use of multiat-tribute transforms to predict Log properties from seismic data[J].Geophysics,2001,66(1):220-236.
8吴大奎,李亚林,伍志明,等.地震、测井资料联合反演的神经网络算法研究[J].天然气工业,2004,24(3):55-57.
9徐明华,王绪本,李学华.自然伽马反演在双家坝构造飞仙关组鲕滩储层预测中的应用[J].石油物探,2003,42(3):346-349.
10Fernando A,Husam M,Patrick M.Pseudo-gamma ray volume from extended elastic impedance inversion for gas ex-ploration[J].The Leading Edge,2004,23:536-540.
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