Oil PVT characterisation using ensemble systems

Munirudeen Oloso, Mohamed Hassan Sayed, James Buick, Mohamed Bader-El-Den

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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In reservoir engineering, there is always a need to estimate crude oil Pressure, Volume and Temperature (PVT) properties for many critical calculations and decisions such as reserve estimate, material balance design and oil recovery strategy, among others. Empirical correlation are often used instead of costly laboratory experiments to estimate these properties. However, these correlations do not always give sufficient accuracy. This paper develops ensemble support vector regression and ensemble regression tree models to predict two important crude oil PVT properties: bubblepoint pressure and oil formation volume factor at bubblepoint. The developed ensemble models are compared with standalone support vector machine (SVM) and regression tree models, and commonly used empirical correlations .The ensemble models give better accuracy when compared to correlations from the literature and more consistent results than the standalone SVM and regression tree models.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Machine Learning and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)978-1509003907
ISBN (Print)978-1509003914
Publication statusPublished - 23 Feb 2017
Event15th International Conference on Machine Learning and Cybernetics - Adelaide, Australia, Jeju Island, Korea, Republic of
Duration: 10 Jul 201613 Jul 2016

Publication series

NameIEEE ICMLC Proceedings Series
ISSN (Electronic)2160-1348


Conference15th International Conference on Machine Learning and Cybernetics
Abbreviated titleICMLC 2016
Country/TerritoryKorea, Republic of
CityJeju Island
Internet address


  • regression tree analysis
  • oils
  • support vector machines
  • correlation
  • predictive models
  • reservoirs
  • prediction algorithms


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