地震数据油气预测中的属性优化方法
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摘要
本文简单介绍了智能信息处理中新出现的RoughSet（RS）理论及属性选择方法，从双相介质地震波传播理论角度，探讨了地震数据油气预测属性优化原理，提出了基于RS理论的地震数据油气预测属性优化方法。实际应用表明；本方法速度快、易实现，而且在优选属性、最大程度地减少提取地震属性种数、提高分类正确率等方面，明显优于其它方法。本方法将成为地震数据油气预测的一种有效手段。
In the paper we briefly introduce the Rough Set (RS) theory, which is a new theory in intelligent information processing, and the attribllte selection method. From the angle of seismic wave propagation theory in two-phase media, we rnake an approach to the attribute optimization principle in oil and gas prediction with seismic data and put forward an attribute optimization method on the basis of the RS theory. The application result indicates that the rnethod is fast in speed and can be realized easily. Moreover, the method is superior to other methods in selecting the attribute, decreasing the number of seismic attributes to be extracted, and improving the classification correctness. It can become an effective means for oil and gas prediction with seismic data.
In the paper we briefly introduce the Rough Set (RS) theory, which is a new theory in intelligent information processing, and the attribllte selection method. From the angle of seismic wave propagation theory in two-phase media, we rnake an approach to the attribute optimization principle in oil and gas prediction with seismic data and put forward an attribute optimization method on the basis of the RS theory. The application result indicates that the rnethod is fast in speed and can be realized easily. Moreover, the method is superior to other methods in selecting the attribute, decreasing the number of seismic attributes to be extracted, and improving the classification correctness. It can become an effective means for oil and gas prediction with seismic data.
引文
[1] 刘企英,《利用地震信息进行油气预测》,石油工业出版社,1994年。
[2] Maher S.Makled,Roger O.Regan,Pattern recognition theory and application to reservoiridentification,Presented at the 3rd Ann.Internat.Mtg,Soc.Geophys,Expanded Abstract,1993,230～233.
[3] Bois P,Autoregressive pattern recognition applied to the delimitation of oil and gasreservoirs,Geophysical Prospecting,1980,28(4),572～291.
[4] 钱绍新,应用模式识别方法预测油气储集层,《地球物理学报》,1992,35(5):630～636。
[5] 朱广生、刘瑞林,神经网络在油气层横向预测及地震道编辑中的应用,《石油物探》,1994,33(4):1～9。
[6] 代俊堂、钱绍新,地震资料的统计模式识别和神经网络模式识别,《石油学报》,1997,18(2):18～27。
[7] 陈遵德等,Kohonen网络在储层预测中的应用,《石油物探》,1995,34(2):53～56。
[8] Pawlak Z.,Rough Set,Theoretical aspacts of reasoning about data,Kluwer Academic Pub,1991.
[9] Pawhk Z. ,Vagueness and uncertainty: a rough set perspective, Inter J. of ComputationalIntelligence,1995,11(2):227～232
[10] 王珏等,关于 Rough Set理论与应用综述,《模式识别与人工智能》,1996,9(4):337～344。
[11] 曾黄麟,《粗集理论及其应用》,重庆:重庆大学出版社,1996年。
[12] 牟永光,《储层地球物理学》,石油工业出版社,1996年。
[2] Maher S.Makled,Roger O.Regan,Pattern recognition theory and application to reservoiridentification,Presented at the 3rd Ann.Internat.Mtg,Soc.Geophys,Expanded Abstract,1993,230～233.
[3] Bois P,Autoregressive pattern recognition applied to the delimitation of oil and gasreservoirs,Geophysical Prospecting,1980,28(4),572～291.
[4] 钱绍新,应用模式识别方法预测油气储集层,《地球物理学报》,1992,35(5):630～636。
[5] 朱广生、刘瑞林,神经网络在油气层横向预测及地震道编辑中的应用,《石油物探》,1994,33(4):1～9。
[6] 代俊堂、钱绍新,地震资料的统计模式识别和神经网络模式识别,《石油学报》,1997,18(2):18～27。
[7] 陈遵德等,Kohonen网络在储层预测中的应用,《石油物探》,1995,34(2):53～56。
[8] Pawlak Z.,Rough Set,Theoretical aspacts of reasoning about data,Kluwer Academic Pub,1991.
[9] Pawhk Z. ,Vagueness and uncertainty: a rough set perspective, Inter J. of ComputationalIntelligence,1995,11(2):227～232
[10] 王珏等,关于 Rough Set理论与应用综述,《模式识别与人工智能》,1996,9(4):337～344。
[11] 曾黄麟,《粗集理论及其应用》,重庆:重庆大学出版社,1996年。
[12] 牟永光,《储层地球物理学》,石油工业出版社,1996年。
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