基于地震属性优选与支持向量机的油气预测方法
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摘要
地震属性分析技术是油气藏勘探开发中的主要研究内容。在利用地震属性进行油气预测前,必须优选出对研究区块油气敏感、彼此相关性不强的属性组。本文针对支持向量机提出一种新的特征选择算法,通过定义核特征相似度推导出核空间类可分性度量,并根据类可分性的变化递归选择最具判别能力的属性子集。将本文算法与支持向量机结合应用于四川观音场构造阳新统上部碳酸盐岩储层和大庆油田G开发区块的油气预测,预测结果验证了本文方法的有效性,可以成为油气预测中的一种可选方法。
Seismic attribute analysis technique is major studied content in oil/gas reservoir exploration and development. It should optimize the attribute group that is sensitive to oil and gas in work zone and no strong cross-correlation before carrying out the oil/gas prediction. The paper presented a new feature-selecting algorithm based on support vector machine. The class separability of kernel space is deduced by defining kernel feature similarity. The subset of attributes having most discriminating ability is selected iteratively based on the variation of class separability. In combination with support vector machine, the algorithm presented in the paper was applied to the issue of oil/gas prediction for Upper Yangxin Series carbonate reservoir in Sichuan Guanyinchang structure and G development block in Daqing Oilfield respectively. The predicted results proved the effectiveness of the method in the paper, which is able to become optional method in oil/gas prediction.
Seismic attribute analysis technique is major studied content in oil/gas reservoir exploration and development. It should optimize the attribute group that is sensitive to oil and gas in work zone and no strong cross-correlation before carrying out the oil/gas prediction. The paper presented a new feature-selecting algorithm based on support vector machine. The class separability of kernel space is deduced by defining kernel feature similarity. The subset of attributes having most discriminating ability is selected iteratively based on the variation of class separability. In combination with support vector machine, the algorithm presented in the paper was applied to the issue of oil/gas prediction for Upper Yangxin Series carbonate reservoir in Sichuan Guanyinchang structure and G development block in Daqing Oilfield respectively. The predicted results proved the effectiveness of the method in the paper, which is able to become optional method in oil/gas prediction.
引文
[1]肖辞源,朱白文.综合多种地震信息预测油气富集区的模糊数学方法.石油地球物理勘探,1990,25(2):191~200
[2]何平,潘国峰.模糊ISODATA算法改进及在油气预测中的应用.河北工业大学学报,2002,32(2):101~105
[3]刘力辉,熊金良,姚江波.油气藏的灰色预测方法.石油地球物理勘探,1994,29(增2):93~97
[4]徐旺林,庞雄奇,吕淑英,Berthold M R.动态概率神经网络及油气概率分布预测.石油地球物理勘探,2005,49(1):65~70
[5]Vapnik V.Statistical Learning Theory.New York:John Wiley&Sons,1998
[6]陈遵德.储层地震属性优化方法.北京:石油工业出版社,1996
[7]印兴耀,周静毅.地震属性优化方法综述.石油地球物理勘探,2005,40(4):284~289
[8]蔡煜东.应用人工神经网络方法预测油气.石油地球物理勘探,1993,28(5):634~638
[9]许建华,蔡瑞.有监督SOM神经网络在油气预测中的应用.石油物探,1998,37(1):71~76
[10]许建华,张学工,李衍达.应用核Fisher判别技术预测油气储集层.石油地球物理勘探,2002,37(2):170~174
[2]何平,潘国峰.模糊ISODATA算法改进及在油气预测中的应用.河北工业大学学报,2002,32(2):101~105
[3]刘力辉,熊金良,姚江波.油气藏的灰色预测方法.石油地球物理勘探,1994,29(增2):93~97
[4]徐旺林,庞雄奇,吕淑英,Berthold M R.动态概率神经网络及油气概率分布预测.石油地球物理勘探,2005,49(1):65~70
[5]Vapnik V.Statistical Learning Theory.New York:John Wiley&Sons,1998
[6]陈遵德.储层地震属性优化方法.北京:石油工业出版社,1996
[7]印兴耀,周静毅.地震属性优化方法综述.石油地球物理勘探,2005,40(4):284~289
[8]蔡煜东.应用人工神经网络方法预测油气.石油地球物理勘探,1993,28(5):634~638
[9]许建华,蔡瑞.有监督SOM神经网络在油气预测中的应用.石油物探,1998,37(1):71~76
[10]许建华,张学工,李衍达.应用核Fisher判别技术预测油气储集层.石油地球物理勘探,2002,37(2):170~174
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