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Oil PVT characterisation using ensemble systems

Oil PVT characterisation using ensemble systems
Oil PVT characterisation using ensemble systems
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.
regression tree analysis, oils, support vector machines, correlation, predictive models, reservoirs, prediction algorithms
61-68
IEEE
Oloso, Munirudeen
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Buick, James
c2b2f76c-1e64-43dc-8b57-d1fe018e1560
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50
Oloso, Munirudeen
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Buick, James
c2b2f76c-1e64-43dc-8b57-d1fe018e1560
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50

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).

Record type: Conference or Workshop Item (Paper)

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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More information

e-pub ahead of print date: 23 February 2017
Published date: 23 February 2017
Venue - Dates: IEEE 2016 International Conference on Machine Learning and Cybernetics, Korea, Republic of, 2016-07-10 - 2016-07-13
Keywords: regression tree analysis, oils, support vector machines, correlation, predictive models, reservoirs, prediction algorithms

Identifiers

Local EPrints ID: 438241
URI: http://eprints.soton.ac.uk/id/eprint/438241
PURE UUID: 8110d9d6-b044-44de-94f1-0638a6e51c23
ORCID for Mohamed Hassan Sayed: ORCID iD orcid.org/0000-0003-3729-4543

Catalogue record

Date deposited: 04 Mar 2020 17:31
Last modified: 30 Jun 2020 00:47

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Contributors

Author: Munirudeen Oloso
Author: Mohamed Hassan Sayed ORCID iD
Author: James Buick
Author: Mohamed Bader-El-Den

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