Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost bli...

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Autores principales:	Shokufe Afzali, Mohamad Mohamadi-Baghmolaei, Sohrab Zendehboudi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	WAG injection gene expression programing statistical analysis empirical correlation oil recovery Technology T
Acceso en línea:	https://doaj.org/article/23a7a9b3a56d462f809ebc487c38e3cd
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spelling	oai:doaj.org-article:23a7a9b3a56d462f809ebc487c38e3cd2021-11-11T15:55:15ZApplication of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process10.3390/en142171311996-1073https://doaj.org/article/23a7a9b3a56d462f809ebc487c38e3cd2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7131https://doaj.org/toc/1996-1073Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>π</mi></semantics></math></inline-formula> theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to <i>R</i><sup>2</sup> = 92.85% and <i>MSE</i> = 1.38 × 10<sup>−3</sup> and <i>R</i><sup>2</sup> = 91.93% and <i>MSE</i> = 4.30 × 10<sup>−3</sup> are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in <i>RF</i> in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the <i>RF</i> of each cycle. It is found that oil to gas viscosity ratio has a higher impact on <i>RF</i> value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.Shokufe AfzaliMohamad Mohamadi-BaghmolaeiSohrab ZendehboudiMDPI AGarticleWAG injectiongene expression programingstatistical analysisempirical correlationoil recoveryTechnologyTENEnergies, Vol 14, Iss 7131, p 7131 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	WAG injection gene expression programing statistical analysis empirical correlation oil recovery Technology T
spellingShingle	WAG injection gene expression programing statistical analysis empirical correlation oil recovery Technology T Shokufe Afzali Mohamad Mohamadi-Baghmolaei Sohrab Zendehboudi Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process
description	Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>π</mi></semantics></math></inline-formula> theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to <i>R</i><sup>2</sup> = 92.85% and <i>MSE</i> = 1.38 × 10<sup>−3</sup> and <i>R</i><sup>2</sup> = 91.93% and <i>MSE</i> = 4.30 × 10<sup>−3</sup> are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in <i>RF</i> in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the <i>RF</i> of each cycle. It is found that oil to gas viscosity ratio has a higher impact on <i>RF</i> value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.
format	article
author	Shokufe Afzali Mohamad Mohamadi-Baghmolaei Sohrab Zendehboudi
author_facet	Shokufe Afzali Mohamad Mohamadi-Baghmolaei Sohrab Zendehboudi
author_sort	Shokufe Afzali
title	Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process
title_short	Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process
title_full	Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process
title_fullStr	Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process
title_full_unstemmed	Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process
title_sort	application of gene expression programming (gep) in modeling hydrocarbon recovery in wag injection process
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/23a7a9b3a56d462f809ebc487c38e3cd
work_keys_str_mv	AT shokufeafzali applicationofgeneexpressionprogramminggepinmodelinghydrocarbonrecoveryinwaginjectionprocess AT mohamadmohamadibaghmolaei applicationofgeneexpressionprogramminggepinmodelinghydrocarbonrecoveryinwaginjectionprocess AT sohrabzendehboudi applicationofgeneexpressionprogramminggepinmodelinghydrocarbonrecoveryinwaginjectionprocess
_version_	1718432843470733312

Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

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