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Oil PVT characterisation using ensemble systems

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

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Oil PVT characterisation using ensemble systems. / Oloso, Munirudeen; Hassan Sayed, Mohamed; Buick, James; Bader-El-Den, Mohamed.

Proceedings of the 2016 International Conference on Machine Learning and Cybernetics. IEEE, 2017. (IEEE ICMLC Proceedings Series).

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

Harvard

Oloso, M, Hassan Sayed, M, Buick, J & Bader-El-Den, M 2017, Oil PVT characterisation using ensemble systems. in Proceedings of the 2016 International Conference on Machine Learning and Cybernetics. IEEE ICMLC Proceedings Series, IEEE, 15th International Conference on Machine Learning and Cybernetics, Jeju Island, Korea, Republic of, 10/07/16. https://doi.org/10.1109/ICMLC.2016.7860878

APA

Oloso, M., Hassan Sayed, M., Buick, J., & Bader-El-Den, M. (2017). Oil PVT characterisation using ensemble systems. In Proceedings of the 2016 International Conference on Machine Learning and Cybernetics (IEEE ICMLC Proceedings Series). IEEE. https://doi.org/10.1109/ICMLC.2016.7860878

Vancouver

Oloso M, Hassan Sayed M, Buick J, Bader-El-Den M. Oil PVT characterisation using ensemble systems. In Proceedings of the 2016 International Conference on Machine Learning and Cybernetics. IEEE. 2017. (IEEE ICMLC Proceedings Series). https://doi.org/10.1109/ICMLC.2016.7860878

Author

Oloso, Munirudeen ; Hassan Sayed, Mohamed ; Buick, James ; Bader-El-Den, Mohamed. / Oil PVT characterisation using ensemble systems. Proceedings of the 2016 International Conference on Machine Learning and Cybernetics. IEEE, 2017. (IEEE ICMLC Proceedings Series).

Bibtex

@inproceedings{33440d3e261747cb9455cc71ce6d23a5,
title = "Oil PVT characterisation using ensemble systems",
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.",
keywords = "regression tree analysis, oils, support vector machines, correlation, predictive models, reservoirs, prediction algorithms",
author = "Munirudeen Oloso and {Hassan Sayed}, Mohamed and James Buick and Mohamed Bader-El-Den",
year = "2017",
month = feb,
day = "23",
doi = "10.1109/ICMLC.2016.7860878",
language = "English",
isbn = "978-1509003914",
series = "IEEE ICMLC Proceedings Series",
publisher = "IEEE",
booktitle = "Proceedings of the 2016 International Conference on Machine Learning and Cybernetics",
note = "15th International Conference on Machine Learning and Cybernetics, ICMLC 2016 ; Conference date: 10-07-2016 Through 13-07-2016",
url = "http://www.icmlc.com/",

}

RIS

TY - GEN

T1 - Oil PVT characterisation using ensemble systems

AU - Oloso, Munirudeen

AU - Hassan Sayed, Mohamed

AU - Buick, James

AU - Bader-El-Den, Mohamed

PY - 2017/2/23

Y1 - 2017/2/23

N2 - 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.

AB - 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.

KW - regression tree analysis

KW - oils

KW - support vector machines

KW - correlation

KW - predictive models

KW - reservoirs

KW - prediction algorithms

U2 - 10.1109/ICMLC.2016.7860878

DO - 10.1109/ICMLC.2016.7860878

M3 - Conference contribution

SN - 978-1509003914

T3 - IEEE ICMLC Proceedings Series

BT - Proceedings of the 2016 International Conference on Machine Learning and Cybernetics

PB - IEEE

T2 - 15th International Conference on Machine Learning and Cybernetics

Y2 - 10 July 2016 through 13 July 2016

ER -

ID: 5135010