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Ensemble SVM for characterisation of crude oil viscosity

Ensemble SVM for characterisation of crude oil viscosity
Ensemble SVM for characterisation of crude oil viscosity

This paper develops ensemble machine learning model for the prediction of dead oil, saturated and undersaturated viscosities. Easily acquired field data have been used as the input parameters for the machine learning process. Different functional forms for each property have been considered in the simulation. Prediction performance of the ensemble model is better than the compared commonly used correlations based on the error statistical analysis. This work also gives insight into the reliability and performance of different functional forms that have been used in the literature to formulate these viscosities. As the improved predictions of viscosity are always craved for, the developed ensemble support vector regression models could potentially replace the empirical correlation for viscosity prediction.

Black oil, Bubble point, Dead oil, Empirical, Ensemble, PVT, Undersaturated, Viscosity
2190-0558
531-546
Oloso, Munirudeen A.
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan, Mohamed G.
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed B.
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James M.
c2b2f76c-1e64-43dc-8b57-d1fe018e1560
Oloso, Munirudeen A.
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan, Mohamed G.
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed B.
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James M.
c2b2f76c-1e64-43dc-8b57-d1fe018e1560

Oloso, Munirudeen A., Hassan, Mohamed G., Bader-El-Den, Mohamed B. and Buick, James M. (2017) Ensemble SVM for characterisation of crude oil viscosity. Journal of Petroleum Exploration and Production Technology, 8 (2), 531-546. (doi:10.1007/s13202-017-0355-x).

Record type: Article

Abstract

This paper develops ensemble machine learning model for the prediction of dead oil, saturated and undersaturated viscosities. Easily acquired field data have been used as the input parameters for the machine learning process. Different functional forms for each property have been considered in the simulation. Prediction performance of the ensemble model is better than the compared commonly used correlations based on the error statistical analysis. This work also gives insight into the reliability and performance of different functional forms that have been used in the literature to formulate these viscosities. As the improved predictions of viscosity are always craved for, the developed ensemble support vector regression models could potentially replace the empirical correlation for viscosity prediction.

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Accepted/In Press date: 14 May 2017
Published date: 1 June 2017
Keywords: Black oil, Bubble point, Dead oil, Empirical, Ensemble, PVT, Undersaturated, Viscosity

Identifiers

Local EPrints ID: 438245
URI: http://eprints.soton.ac.uk/id/eprint/438245
ISSN: 2190-0558
PURE UUID: 370af07f-9fb9-42d5-94c0-e318fa325b60
ORCID for Mohamed G. Hassan: ORCID iD orcid.org/0000-0003-3729-4543

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Date deposited: 04 Mar 2020 17:31
Last modified: 07 Oct 2020 02:27

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Contributors

Author: Munirudeen A. Oloso
Author: Mohamed G. Hassan ORCID iD
Author: Mohamed B. Bader-El-Den
Author: James M. Buick

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