基于多地震属性的储层预测新方法  

A New Method of Reservoir Prediction Based on Seismic Attributes

作　　者：朱永才 尹成[2] 薛坤林[2] 赵龙 

机构地区：[1]新疆油田公司勘探开发研究院勘探所,新疆克拉玛依834000 [2]西南石油大学,四川成都610500

出　　处：《广东石油化工学院学报》2014年第1期63-66,71,共5页Journal of Guangdong University of Petrochemical Technology

基　　金：国家高技术研究发展计划"863"项目(2006AA09A102-14);国家科技重大专项课题(2008ZX05024-001)

摘　　要：目前采用地震属性预测储层参数的方法层出不穷,但是这些方法多数是基于单变量、线性的机器学习算法,在已知样本较少的情况下精度得不到保证。为了获取高精度的储层参数,指导油气的勘探开发,迫切需要寻求一种新的方法最大限度地挖掘地震地质信息。支持向量机是以结构风险最小化原则为核心的新型机器学习算法,与传统的机器学习算法相比,其具有基于多变量、小样本、非线性和预测精度高的优点。以渤海湾SZ-361油田Ⅰ油组顶部储层参数预测为例,采用支持向量机算法,得到了较高精度的储层预测结果,证实了支持向量机算法可以应用于油气勘探领域。The methods of predicting reservoir parameters based on the seismic attributes emerge in an endless stream at present .However , these methods are almost based on single variable and linear algorithm ,and the accuracy could not be ensured because there are not enough samples .In order to obtain reservoir parameters with high accuracy which are used to guide the exploration and development of oil and gas , it is necessary to find a new method to get maximum seismic and geological information .SVM is a new machine learning algorithm which is based on the principle of minimum structural risk .Compared with traditional machine learning algorithm ,SVM have several advantages such as multi-variable- based ,small sample ,nonlinear and high accuracy of prediction .This paper presents an example of predicting reser-voir parameters at the top ofⅠoil group in SZ-361 oilfield in Bohai bay and obtains a more rigorous result by SVM .This research indicated that SVM can be applied to the exploration and development of oil and gas .

关 键 词：地震属性 支持向量机 储层参数 结构风险最小化原则 SUPPORT VECTOR MACHINE (SVM ) 

分 类 号：P618[天文地球—矿床学]