Application of support vector machine in drag reduction effect prediction of nanoparticles adsorption method on oil reservoir's micro-channels

被引:1
|
作者
狄勤丰 [1 ,2 ]
华帅 [1 ,2 ]
丁伟朋 [1 ]
龚伟 [2 ]
程毅翀 [1 ,2 ]
叶峰 [1 ,2 ]
机构
[1] Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University
[2] Shanghai Key Laboratory of Mechanics in Energy and Environment Engineering
基金
中国国家自然科学基金;
关键词
nanoparticles adsorption method; support vector machine(SVM); prediction model; rapid evaluation; enhanced oil recovery;
D O I
暂无
中图分类号
TE357 [提高采收率与维持油层压力(二次、三次采油)]; O647.3 [吸附];
学科分类号
070304 ; 081704 ; 082002 ;
摘要
Due to the complexity of influence factors in the nanoparticles adsorption method and the limitation of data samples, the support vector machine(SVM) was used in the prediction method for the drag reduction effect. The basic concept of SVM was introduced, and the ε- SVR programming for the kernel function on the radial basis was established firstly with the help of the MATLAB software. Then, an analysis was made for the influencing factors of the drag reduction effect in nanoparticles adsorption. Finally, a prediction model for the drag reduction effect of nanoparticles was established, and the accuracy of training sample and prediction sample was analyzed. The result shows that the SVM has good availability and can be used as a rapid evaluation method of the drag reduction effect prediction of nanoparticles adsorption method.
引用
收藏
页码:99 / 104
页数:6
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