Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques
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- 作者：Amin Gholami ; Hamid Reza Ansari…
- 关键词：Refractive index (RI) ; Support vector regression (SVR) ; Hybrid grid and pattern search (HGP) ; Genetic algorithm (GA) ; Imperialist competitive algorithm (ICA)
- 刊名：Journal of Petroleum Exploration and Production Technology
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：7
- 期：1
- 页码：195-204
- 全文大小：1311KB
- 刊物主题：Geology; Industrial and Production Engineering; Energy Technology; Offshore Engineering; Industrial Chemistry/Chemical Engineering; Monitoring/Environmental Analysis;
- 出版者：Springer Berlin Heidelberg
- ISSN：2190-0566
- 卷排序：7
文摘
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 R2 of 0.9971 and MSE of 1.48548e−05 demonstrating its efficacy in obtaining crude oil refractive index form SARA data.