Hybrid connectionist model determines CO2–oil swelling factor

Abstract In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery (EOR) process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the...

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Autores principales:	Mohammad Ali Ahmadi, Sohrab Zendehboudi, Lesley A. James
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2018
Materias:	CO2 injection CO2 swelling Genetic algorithm Predictive model Least-squares support vector machine Science Q Petrology QE420-499
Acceso en línea:	https://doaj.org/article/e2abfd6e78f14fe7b10071634ab0972f
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spelling	oai:doaj.org-article:e2abfd6e78f14fe7b10071634ab0972f2021-12-02T03:12:20ZHybrid connectionist model determines CO2–oil swelling factor10.1007/s12182-018-0230-51672-51071995-8226https://doaj.org/article/e2abfd6e78f14fe7b10071634ab0972f2018-04-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-018-0230-5https://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstract In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery (EOR) process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine (LS-SVM) to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters (γ and σ 2) of the LS-SVM model. This model showed a high coefficient of determination (R 2 = 0.9953) and a low value for the mean-squared error (MSE = 0.0003) based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.Mohammad Ali AhmadiSohrab ZendehboudiLesley A. JamesKeAi Communications Co., Ltd.articleCO2 injectionCO2 swellingGenetic algorithmPredictive modelLeast-squares support vector machineScienceQPetrologyQE420-499ENPetroleum Science, Vol 15, Iss 3, Pp 591-604 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	CO2 injection CO2 swelling Genetic algorithm Predictive model Least-squares support vector machine Science Q Petrology QE420-499
spellingShingle	CO2 injection CO2 swelling Genetic algorithm Predictive model Least-squares support vector machine Science Q Petrology QE420-499 Mohammad Ali Ahmadi Sohrab Zendehboudi Lesley A. James Hybrid connectionist model determines CO2–oil swelling factor
description	Abstract In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery (EOR) process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine (LS-SVM) to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters (γ and σ 2) of the LS-SVM model. This model showed a high coefficient of determination (R 2 = 0.9953) and a low value for the mean-squared error (MSE = 0.0003) based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.
format	article
author	Mohammad Ali Ahmadi Sohrab Zendehboudi Lesley A. James
author_facet	Mohammad Ali Ahmadi Sohrab Zendehboudi Lesley A. James
author_sort	Mohammad Ali Ahmadi
title	Hybrid connectionist model determines CO2–oil swelling factor
title_short	Hybrid connectionist model determines CO2–oil swelling factor
title_full	Hybrid connectionist model determines CO2–oil swelling factor
title_fullStr	Hybrid connectionist model determines CO2–oil swelling factor
title_full_unstemmed	Hybrid connectionist model determines CO2–oil swelling factor
title_sort	hybrid connectionist model determines co2–oil swelling factor
publisher	KeAi Communications Co., Ltd.
publishDate	2018
url	https://doaj.org/article/e2abfd6e78f14fe7b10071634ab0972f
work_keys_str_mv	AT mohammadaliahmadi hybridconnectionistmodeldeterminesco2oilswellingfactor AT sohrabzendehboudi hybridconnectionistmodeldeterminesco2oilswellingfactor AT lesleyajames hybridconnectionistmodeldeterminesco2oilswellingfactor
_version_	1718401891907403776

Hybrid connectionist model determines CO2–oil swelling factor

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