利用支持向量机方法预测储层产能  

PREDICTION OF RESERVOIR PRODUCTIVITY BY SUPPORT VECTOR MACHINE

作　　者：张锋[1] 张星[2] 张乐[3] 郝永卯[1] 单蔚[4] 

机构地区：[1]中国石油大学(华东),山东东营257061 [2]中石化胜利油田采油院,山东东营257000 [3]胜利油田井下作业公司海洋试油作业大队 [4]中石化胜利油田海洋采油厂

出　　处：《西南石油大学学报（自然科学版）》2007年第3期24-27,共4页Journal of Southwest Petroleum University(Science & Technology Edition)

基　　金：国家863课题资助(2006AA09A209)。

摘　　要：支持向量机方法(SVM)是基于结构风险最小化原理,采用核函数处理技术,较好的适应小样本、非线性和局部极小点等实际问题,克服了常规统计方法的局限性,避免了维数灾难。能够在有限的样本集基础上,兼顾模型的通用性和推广性,有效解决了学习性和延拓性的问题,预测精度更高。实际生产中影响储层产能因素众多,各因素间相互影响,在综合考虑地层因素的基础上,提取了测井产能预测参数,利用支持向量机方法对产能进行了预测,预测结果与实际一致,并将处理结果与多元回归及BP神经网络处理结果进行了对比分析。实践表明支持向量机方法优于后两种方法,是一种值得推广使用的方法。Support vector machine method is based on the principle of structural risk minimization, and suitable for small samples, nonlinearity and local minimun by Kernel function processing technology, it overcomes the limitation of conventional statistical method, avoids dimentionality disaster, compromises the commonality and popurization of the model on the basis of limited samples and solves the problems of learning performance and popularization effectively with higher prediction accuracy. In practical production, there are a lots factors influncing reservoir productivity, and the factors impact each other. In view of comprehensively considering formation factors, well logging productivity prediction parameters are sorted out, the productivity is predicted by support vector machine, the prediction result is correlated and compareed with the result from multi-regression and BP neural network processing. Practice suggests that the support vector machine is better than the latter two methods and is worthyto be popularized.

关 键 词：支持向量机 结构风险最小化 学习性 推广性 产能预测 

分 类 号：TE319[石油与天然气工程—油气田开发工程]