On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches
Gas injection has emerged over the recent decades as a promising technology to enhance oil recovery in various fields worldwide. The efficiency and success of a gas injection operation can be assessed through a number of vital experimental studies. Interfacial Tension (IFT) between the injected gas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682200312X |
| _version_ | 1797978761994436608 |
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| author | Mehdi Mahdaviara Menad Nait Amar Mehdi Ostadhassan Abdolhossein Hemmati-Sarapardeh |
| author_facet | Mehdi Mahdaviara Menad Nait Amar Mehdi Ostadhassan Abdolhossein Hemmati-Sarapardeh |
| author_sort | Mehdi Mahdaviara |
| collection | DOAJ |
| description | Gas injection has emerged over the recent decades as a promising technology to enhance oil recovery in various fields worldwide. The efficiency and success of a gas injection operation can be assessed through a number of vital experimental studies. Interfacial Tension (IFT) between the injected gas and the displacing fluid is a key parameter playing an eminent role in the foregoing studies. The main scope of this work is making a progress in modeling the IFTs between diverse n-alkanes and Methane (CH4), Carbon Dioxide (CO2), and Nitrogen (N2) natural gases. For this purpose, two smart AI-based approaches of Cascaded Feedforward Neural Network (CFNN) and Decision Tree Learning (DT) were used to simultaneously model the IFTs between foregoing immiscible binary systems as a function of pressure, temperature, the gases properties, and the properties of the liquid. Several statistical measures and graphical descriptions were employed to aid the accuracy analysis of the proposed models. Both developed CFNN and DT networks represented desirable close-to-reality predictions in all binary systems. Besides, CFNN established itself as the most robust model for all studies binary systems with RMSE values of 0.5924, 0.5649, and 0.5870 mN/m, and R2 values of 0.9902, 0.9910, and 0.9904 for the train, test, and overall data, respectively. |
| first_indexed | 2024-04-11T05:29:15Z |
| format | Article |
| id | doaj.art-66c4260221724e37a930c1d773bb4cbf |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-11T05:29:15Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-66c4260221724e37a930c1d773bb4cbf2022-12-23T04:38:56ZengElsevierAlexandria Engineering Journal1110-01682022-12-0161121160111614On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approachesMehdi Mahdaviara0Menad Nait Amar1Mehdi Ostadhassan2Abdolhossein Hemmati-Sarapardeh3Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, IranDépartement Etudes Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes, AlgeriaInstitute of Geosciences, Marine and Land Geomechanics and Geotectonics, Christian-Albrechts-Universität, Kiel 24118, Germany; State Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, China; Department of Geology, Ferdowsi University of Mashhad, Mashhad, Iran; Corresponding authors at: Institute of Geosciences, Marine and Land Geomechanics and Geotectonics, Christian-Albrechts-Universität, Kiel 24118, Germany (M. Ostadhassan) Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran (A. Hemmati-Sarapardeh).Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran; College of Construction Engineering, Jilin University, Changchun 130012, PR China; Corresponding authors at: Institute of Geosciences, Marine and Land Geomechanics and Geotectonics, Christian-Albrechts-Universität, Kiel 24118, Germany (M. Ostadhassan) Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran (A. Hemmati-Sarapardeh).Gas injection has emerged over the recent decades as a promising technology to enhance oil recovery in various fields worldwide. The efficiency and success of a gas injection operation can be assessed through a number of vital experimental studies. Interfacial Tension (IFT) between the injected gas and the displacing fluid is a key parameter playing an eminent role in the foregoing studies. The main scope of this work is making a progress in modeling the IFTs between diverse n-alkanes and Methane (CH4), Carbon Dioxide (CO2), and Nitrogen (N2) natural gases. For this purpose, two smart AI-based approaches of Cascaded Feedforward Neural Network (CFNN) and Decision Tree Learning (DT) were used to simultaneously model the IFTs between foregoing immiscible binary systems as a function of pressure, temperature, the gases properties, and the properties of the liquid. Several statistical measures and graphical descriptions were employed to aid the accuracy analysis of the proposed models. Both developed CFNN and DT networks represented desirable close-to-reality predictions in all binary systems. Besides, CFNN established itself as the most robust model for all studies binary systems with RMSE values of 0.5924, 0.5649, and 0.5870 mN/m, and R2 values of 0.9902, 0.9910, and 0.9904 for the train, test, and overall data, respectively.http://www.sciencedirect.com/science/article/pii/S111001682200312XNatural gasInterfacial tension (IFT)n-alkanesCascaded Feedforward Neural Network (CFNN)Decision Tree Learning (DT) |
| spellingShingle | Mehdi Mahdaviara Menad Nait Amar Mehdi Ostadhassan Abdolhossein Hemmati-Sarapardeh On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches Alexandria Engineering Journal Natural gas Interfacial tension (IFT) n-alkanes Cascaded Feedforward Neural Network (CFNN) Decision Tree Learning (DT) |
| title | On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches |
| title_full | On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches |
| title_fullStr | On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches |
| title_full_unstemmed | On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches |
| title_short | On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches |
| title_sort | on the evaluation of the interfacial tension of immiscible binary systems of methane carbon dioxide and nitrogen alkanes using robust data driven approaches |
| topic | Natural gas Interfacial tension (IFT) n-alkanes Cascaded Feedforward Neural Network (CFNN) Decision Tree Learning (DT) |
| url | http://www.sciencedirect.com/science/article/pii/S111001682200312X |
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