基于支持向量机的地震多属性综合解释技术应用研究
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摘要
给出基于支持向量机方法的储层参数预测流程。以粗糙集理论优选出的地震属性为输入向量,利用支持向量机方法对渤海SZ36-1的砂泥岩百分比、孔隙度与含油饱和度进行预测。实例应用表明,利用此方法预测储层参数的精度较高。
This theme gives the process to predict reservoir parameters based on the support vector machine.We predictes the percentage of sandstone to mudstone,porosity and oil saturation of SZ36-1 by the support vector machine combined with seismic attributes which are optimizated by the Rough set theory as the input vector.The results show that the method has higher prediction accuracy to predict reservoir parameters.
This theme gives the process to predict reservoir parameters based on the support vector machine.We predictes the percentage of sandstone to mudstone,porosity and oil saturation of SZ36-1 by the support vector machine combined with seismic attributes which are optimizated by the Rough set theory as the input vector.The results show that the method has higher prediction accuracy to predict reservoir parameters.
引文
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[2]Platt J C.Fast Training of Support Vector Machines UsingSequential Minimal Optimization[C]//In Sch.o.lkoph B,Burges C,Smola A J.Advances in Kernel Methods-SupportVector Machines Learning.Cambridge,MA:MIT Press.,1999:185-208.
[3]Smola A J,Sch.o.lkoph B.A Tutorial on Support VectorRegression[R].Royal Holloway College,Neuro COLTTechnical Report.TR-1998-030,1998.
[4]万琳.地震属性分析及其在储层预测中的应用[J].油气地球物理,2009,74(3):43-46.
[5]李在光,杨占龙.多属性综合方法预测含油气性及其效果[J].天然气地球科学,2006,17(5):727-730.
[6]唐耀华,张向君,高静怀.基于地震属性优选与支持向量机的油气预测方法[J].石油地球物理勘探,2009,44(1):75-80.
[7]魏艳,尹成,丁峰,等.地震多属性综合分析的应用研究[J].石油物探,2007,46(1):42-47.
[8]乐友喜,袁全社.支持向量机方法在储层预测中的应用[J].石油物探,2005,44(4):387-392.
[9]朱剑兵,谭明友.基于支持向量机的地震储层参数预测方法初探[J].油气地球物理,2008,6(1):34-37.
[10]杜树新,吴铁军.用于回归估计的支持向量机方法[J].系统仿真学报,2003,15(11):1580-1633.Multi-attribute Seismic Comprehensive Interpretation and ApplicationTechnology Based on Support Vector MachineZHAO Yong1 GAO Li-qing2 ZHANG Shan3 LI Hang4(1.Southwest Petroleum University,Chengdu 6105002;2.Qinghe Qil Production factory,Jianghan Oilfield
[2]Platt J C.Fast Training of Support Vector Machines UsingSequential Minimal Optimization[C]//In Sch.o.lkoph B,Burges C,Smola A J.Advances in Kernel Methods-SupportVector Machines Learning.Cambridge,MA:MIT Press.,1999:185-208.
[3]Smola A J,Sch.o.lkoph B.A Tutorial on Support VectorRegression[R].Royal Holloway College,Neuro COLTTechnical Report.TR-1998-030,1998.
[4]万琳.地震属性分析及其在储层预测中的应用[J].油气地球物理,2009,74(3):43-46.
[5]李在光,杨占龙.多属性综合方法预测含油气性及其效果[J].天然气地球科学,2006,17(5):727-730.
[6]唐耀华,张向君,高静怀.基于地震属性优选与支持向量机的油气预测方法[J].石油地球物理勘探,2009,44(1):75-80.
[7]魏艳,尹成,丁峰,等.地震多属性综合分析的应用研究[J].石油物探,2007,46(1):42-47.
[8]乐友喜,袁全社.支持向量机方法在储层预测中的应用[J].石油物探,2005,44(4):387-392.
[9]朱剑兵,谭明友.基于支持向量机的地震储层参数预测方法初探[J].油气地球物理,2008,6(1):34-37.
[10]杜树新,吴铁军.用于回归估计的支持向量机方法[J].系统仿真学报,2003,15(11):1580-1633.Multi-attribute Seismic Comprehensive Interpretation and ApplicationTechnology Based on Support Vector MachineZHAO Yong1 GAO Li-qing2 ZHANG Shan3 LI Hang4(1.Southwest Petroleum University,Chengdu 6105002;2.Qinghe Qil Production factory,Jianghan Oilfield
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