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CRUDE Oil PVT Characterisation using Ensemble Systems

CRUDE Oil PVT Characterisation using Ensemble Systems
CRUDE Oil PVT Characterisation using Ensemble Systems
In reservoir engineering, there is always a need to estimate crude oil pressure-volume-temperature properties for many critical calculations and decisions such as reserve estimate, material balance design and oil recovery strategy, among others. Empirical correlation are still often used instead of the costly laboratory experiments to estimate these properties, while the adoption of machine learning approaches is increasingly getting necessary attention for the industrial applications. However, these correlations may not always give sufficient or consistent 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. Two different experimental works were carried out to train the ensemble models using different datasets. The developed ensemble models are compared with standalone support vector machine (SVM) and regression tree models, and commonly used empirical correlations. Consistency and reliability of the models are explored by testing on three different additional datasets. Training the ensemble models with bigger data set improves their performances. The ensemble regression trees model gives the best overall result, consistency and resilience to the variability of the datasets in testing.
1568-4946
Oloso, Munirudeen
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James
c2b2f76c-1e64-43dc-8b57-d1fe018e1560
Oloso, Munirudeen
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James
c2b2f76c-1e64-43dc-8b57-d1fe018e1560

Oloso, Munirudeen, Hassan Sayed, Mohamed, Bader-El-Den, Mohamed and Buick, James (2016) CRUDE Oil PVT Characterisation using Ensemble Systems. Applied Soft Computing.

Record type: Article

Abstract

In reservoir engineering, there is always a need to estimate crude oil pressure-volume-temperature properties for many critical calculations and decisions such as reserve estimate, material balance design and oil recovery strategy, among others. Empirical correlation are still often used instead of the costly laboratory experiments to estimate these properties, while the adoption of machine learning approaches is increasingly getting necessary attention for the industrial applications. However, these correlations may not always give sufficient or consistent 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. Two different experimental works were carried out to train the ensemble models using different datasets. The developed ensemble models are compared with standalone support vector machine (SVM) and regression tree models, and commonly used empirical correlations. Consistency and reliability of the models are explored by testing on three different additional datasets. Training the ensemble models with bigger data set improves their performances. The ensemble regression trees model gives the best overall result, consistency and resilience to the variability of the datasets in testing.

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Published date: 30 December 2016

Identifiers

Local EPrints ID: 438247
URI: http://eprints.soton.ac.uk/id/eprint/438247
ISSN: 1568-4946
PURE UUID: 2b439b42-9c0e-42da-84c7-ba57f3e9063d
ORCID for Mohamed Hassan Sayed: ORCID iD orcid.org/0000-0003-3729-4543

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Date deposited: 04 Mar 2020 17:31
Last modified: 20 May 2020 01:03

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Contributors

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

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