【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。更多还原

【Abstract】 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.更多还原