Implementation of SVM framework to estimate PVT properties of reservoir oil

Shahin Rafiee-Taghanaki, Milad Arabloo, Ali Chamkalani, Mahmood Amani, Mohammad Hadi Zargari, Mohammad Reza Adelzadeh

Research output: Contribution to journalArticle

99 Citations (Scopus)


Through this work, a novel mathematical-based approach was proposed to develop reliable models for calculation of PVT properties of crude oils at various reservoir conditions. For this purpose, a new soft computing approach namely Least Square Support Vector Machine (LSSVM) modeling optimized with Coupled Simulated Annealing (CSA) optimization technique was implemented. The constructed models are evaluated by carrying out extensive experimental data reported in open literature. Results obtained by the proposed models were compared with the corresponding experimental values. Moreover, in-depth comparative studies have been carried out between these models and all other predictive models. The results indicate that the proposed models are more robust, reliable and efficient than existing techniques for prediction of PVT properties. Results from present research show that implementation of CSA-LSSVM in crude oil PVT calculations can lead to more accurate and reliable estimation of reservoir oil PVT properties.

Original languageEnglish
Pages (from-to)25-32
Number of pages8
JournalFluid Phase Equilibria
Publication statusPublished - 25 May 2013



  • Coupled Simulated Annealing
  • Empirical correlation
  • Least Square Support Vector Machine
  • Oil formation volume factor
  • Saturation pressure

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

Cite this

Rafiee-Taghanaki, S., Arabloo, M., Chamkalani, A., Amani, M., Zargari, M. H., & Adelzadeh, M. R. (2013). Implementation of SVM framework to estimate PVT properties of reservoir oil. Fluid Phase Equilibria, 346, 25-32.