4.6 Article

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s13202-016-0229-7

关键词

Refractive index (RI); Support vector regression (SVR); Hybrid grid and pattern search (HGP); Genetic algorithm (GA); Imperialist competitive algorithm (ICA)

资金

  1. departments of research and technology of the National Iranian Oil Company
  2. Iranian Offshore Oil Company

向作者/读者索取更多资源

Refractive index (RI) provides valuable information about various reservoir engineering calculations, making it a key parameter for characterizing crude oils. Determination of this index through experiment is capital-intensive, time consuming, and also toil. Hence, it is essential to search for an efficient and accurate estimation of crude oil RI. In this study, an intelligent approach, based on optimized support vector regression (SVR), is introduced to find a quantitative correlation between crude oil RI and SARA (saturate, aromatic, resin, and asphaltene) fraction data. Optimization of SVR is implemented through three searching approaches, viz. hybrid of grid and pattern search (HGP), genetic algorithm (GA), and imperialist competitive algorithm (ICA). Using these approaches, three models are constructed and tested on experimental data gathered from open source literature. To evaluate the performance of these models, their outputs are compared with corresponding experimental data in terms of statistical criteria. The comparative study clearly shows the advantage of ICA over its rivals (GA and HGP) in optimizing the SVR parameters. ICA optimized support vector regression results in an R-2 of 0.9971 and MSE of 1.48548e-05 demonstrating its efficacy in obtaining crude oil refractive index form SARA data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据