Sensitivity analysis of low salinity waterflood alternating immiscible CO2 injection (Immiscible CO2-LSWAG) performance using machine learning application in sandstone reservoir.
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| Title: | Sensitivity analysis of low salinity waterflood alternating immiscible CO |
|---|---|
| Authors: | Efras, Muhammad Ridho1 (AUTHOR), Dzulkarnain, Iskandar1,2 (AUTHOR) iskandar_dzulkarnain@utp.edu.my, Ridha, Syahrir1,2 (AUTHOR), Syahputra, Loris Alif3 (AUTHOR), Rasool, Muhammad Hammad3 (AUTHOR), Merdeka, Mohammad Galang4 (AUTHOR), Pramana, Agus Astra5 (AUTHOR) |
| Source: | Journal of Petroleum Exploration & Production Technology. Nov2024, Vol. 14 Issue 11, p3055-3077. 23p. |
| Subject Terms: | *Machine learning, *Enhanced oil recovery, *Field research, *Sensitivity analysis, *Machine performance |
| Abstract: | Low salinity water alternating immiscible gas CO2 (Immiscible CO2-LSWAG) injection is a popular technique for enhanced oil recovery (EOR) that combines the benefits of low salinity and immiscible CO2 flooding to increase and accelerate oil production. This approach modifies the displacement properties of the reservoir, resulting in higher sweep efficiency and greater oil production. The current study employs a combination of numerical and machine learning techniques to comprehensively investigate the performance of immiscible CO2-LSWAG injection in a sandstone reservoir. Furthermore, a detailed sensitivity analysis of various injection and reservoir parameters is conducted to gain deeper insights into their impact on the process. In order to predict the oil recovery factor (RF), the study employs 1000 experimental designs on initial oil-wet. The numerical simulation results indicate that immiscible CO2-LSWAG injection outperforms conventional immiscible CO2 and low salinity waterflood injection, resulting in a higher oil RF. The machine learning models of Catboost and LightGBM used in this study produced R2 scores higher than 0.95 with lower errors between the predicted and actual results. This indicates that machine learning models can provide a faster and more accurate alternative to numerical simulation. The sensitivity analysis results from the machine learning model reveal that the major contributing factors to oil RF are the chemical composition of the injected water and the injection rate. In summary, this study leverages machine learning for sensitivity analysis in immiscible CO2-LSWAG performance in oil-wet sandstone reservoirs. Key findings include the identification of top influencing parameters and high predictive accuracy of CatBoost and LightGBM algorithms. The results facilitate quick decision-making for field trials by focusing on major contributing factors, with future research suggested for broader applications. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 180849330 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sensitivity analysis of low salinity waterflood alternating immiscible CO<subscript>2</subscript> injection (Immiscible CO<subscript>2</subscript>-LSWAG) performance using machine learning application in sandstone reservoir. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Efras%2C+Muhammad+Ridho%22">Efras, Muhammad Ridho</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dzulkarnain%2C+Iskandar%22">Dzulkarnain, Iskandar</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> iskandar_dzulkarnain@utp.edu.my</i><br /><searchLink fieldCode="AR" term="%22Ridha%2C+Syahrir%22">Ridha, Syahrir</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Syahputra%2C+Loris+Alif%22">Syahputra, Loris Alif</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rasool%2C+Muhammad+Hammad%22">Rasool, Muhammad Hammad</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Merdeka%2C+Mohammad+Galang%22">Merdeka, Mohammad Galang</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pramana%2C+Agus+Astra%22">Pramana, Agus Astra</searchLink><relatesTo>5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Petroleum+Exploration+%26+Production+Technology%22">Journal of Petroleum Exploration & Production Technology</searchLink>. Nov2024, Vol. 14 Issue 11, p3055-3077. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Enhanced+oil+recovery%22">Enhanced oil recovery</searchLink><br />*<searchLink fieldCode="DE" term="%22Field+research%22">Field research</searchLink><br />*<searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+performance%22">Machine performance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Low salinity water alternating immiscible gas CO2 (Immiscible CO2-LSWAG) injection is a popular technique for enhanced oil recovery (EOR) that combines the benefits of low salinity and immiscible CO2 flooding to increase and accelerate oil production. This approach modifies the displacement properties of the reservoir, resulting in higher sweep efficiency and greater oil production. The current study employs a combination of numerical and machine learning techniques to comprehensively investigate the performance of immiscible CO2-LSWAG injection in a sandstone reservoir. Furthermore, a detailed sensitivity analysis of various injection and reservoir parameters is conducted to gain deeper insights into their impact on the process. In order to predict the oil recovery factor (RF), the study employs 1000 experimental designs on initial oil-wet. The numerical simulation results indicate that immiscible CO2-LSWAG injection outperforms conventional immiscible CO2 and low salinity waterflood injection, resulting in a higher oil RF. The machine learning models of Catboost and LightGBM used in this study produced R2 scores higher than 0.95 with lower errors between the predicted and actual results. This indicates that machine learning models can provide a faster and more accurate alternative to numerical simulation. The sensitivity analysis results from the machine learning model reveal that the major contributing factors to oil RF are the chemical composition of the injected water and the injection rate. In summary, this study leverages machine learning for sensitivity analysis in immiscible CO2-LSWAG performance in oil-wet sandstone reservoirs. Key findings include the identification of top influencing parameters and high predictive accuracy of CatBoost and LightGBM algorithms. The results facilitate quick decision-making for field trials by focusing on major contributing factors, with future research suggested for broader applications. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s13202-024-01849-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 3055 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Enhanced oil recovery Type: general – SubjectFull: Field research Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Machine performance Type: general Titles: – TitleFull: Sensitivity analysis of low salinity waterflood alternating immiscible CO2 injection (Immiscible CO2-LSWAG) performance using machine learning application in sandstone reservoir. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Efras, Muhammad Ridho – PersonEntity: Name: NameFull: Dzulkarnain, Iskandar – PersonEntity: Name: NameFull: Ridha, Syahrir – PersonEntity: Name: NameFull: Syahputra, Loris Alif – PersonEntity: Name: NameFull: Rasool, Muhammad Hammad – PersonEntity: Name: NameFull: Merdeka, Mohammad Galang – PersonEntity: Name: NameFull: Pramana, Agus Astra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 21900558 Numbering: – Type: volume Value: 14 – Type: issue Value: 11 Titles: – TitleFull: Journal of Petroleum Exploration & Production Technology Type: main |
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