Oil PVT characterisation using ensemble systems

Oloso, Munirudeen, Hassan Sayed, Mohamed, Buick, James and Bader-El-Den, Mohamed (2017) Oil PVT characterisation using ensemble systems. In Proceedings of the 2016 International Conference on Machine Learning and Cybernetics. IEEE. pp. 61-68 . (doi:10.1109/ICMLC.2016.7860878).

Abstract

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.

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Contributors

Author: Mohamed Hassan Sayed

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