Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state
Enhancing efficiency and boosting output from oil reservoirs has consistently captured the attention of engineers and industrialists within the energy sector. In recent years, there has been a notable increase in the application of enhanced oil recovery (EOR) techniques. EOR methods refer to operati...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025001239 |
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| author | Behnam Amiri-Ramsheh Aydin Larestani Saeid Atashrouz Elnaz Nasirzadeh Meriem Essakhraoui Ali Abedi Mehdi Ostadhassan Ahmad Mohaddespour Abdolhossein Hemmati-Sarapardeh |
| author_facet | Behnam Amiri-Ramsheh Aydin Larestani Saeid Atashrouz Elnaz Nasirzadeh Meriem Essakhraoui Ali Abedi Mehdi Ostadhassan Ahmad Mohaddespour Abdolhossein Hemmati-Sarapardeh |
| author_sort | Behnam Amiri-Ramsheh |
| collection | DOAJ |
| description | Enhancing efficiency and boosting output from oil reservoirs has consistently captured the attention of engineers and industrialists within the energy sector. In recent years, there has been a notable increase in the application of enhanced oil recovery (EOR) techniques. EOR methods refer to operations which are designed in order to maximize the oil recovery factor. Among various gas mixtures that are proposed as candidates to be injected into mature oil reservoirs, CO2 gas attains miscibility with the resident hydrocarbon fluid at a reasonable pressure and increases the oil recovery factor. CO2 injection, as an EOR method, has the potential of being coupled with CO2 sequestration and reducing the emission of greenhouse gas. To design a successful CO2 injection process, it is very important to have precise knowledge about the compressibility factor (Z-factor) of CO2 as it directly affects material balance calculations, pipeline design, design of surface facilities, and CO2 compression. Z-factor, also defined as the gas deviation factor, is mathematically explained as the ratio of actual gas volume to that of an ideal gas at a given temperature and pressure. In this study, two powerful and robust tree-based machine learning (ML) algorithms, namely light gradient boosted machine (LightGBM) and extreme gradient boosting (XGBoost) were utilized to precisely estimate CO2 Z-factor. To this end, a comprehensive databank from the literature is employed, which contains 2118 data points over extensive ranges of pressures and temperatures. The proposed models predict the CO2 Z-factor with respect to reduced temperature (Tr) and reduced pressure (Pr). Moreover, the results of the developed techniques were compared with those of Patel-Teja (PT) and Peng-Robinson (PR) equations of state (EoSs) applying various graphical and statistical error tests. The results demonstrated that the LightGBM intelligent technique has the highest accuracy with the lowest error value of 0.42 % and R2 of 0.999. The trend analysis illustrated that the LightGBM model could verify the actual variation of CO2 Z-factor with pressure (direct relationship) and accurately forecast the physical behavior of the CO2 Z-factor variation. Lately, outlier detection utilizing the Leverage approach illustrated that nearly all data points, except only 39 points, were statistically reliable and located in the valid zone. The results of this research can extremely help for better understanding of CO2 sequestration, decreasing the greenhouse gas emission, and exploring EOR techniques especially CO2 injection. |
| format | Article |
| id | doaj-art-cf12bc40247245a38ce5efe93ca53c7b |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-cf12bc40247245a38ce5efe93ca53c7b2025-02-02T05:29:13ZengElsevierResults in Engineering2590-12302025-03-0125104035Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of stateBehnam Amiri-Ramsheh0Aydin Larestani1Saeid Atashrouz2Elnaz Nasirzadeh3Meriem Essakhraoui4Ali Abedi5Mehdi Ostadhassan6Ahmad Mohaddespour7Abdolhossein Hemmati-Sarapardeh8Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranDepartment of Information Technology Management, Faculty of Industrial and Technology Management, College of Management, University of Tehran, Tehran, IranChemistry Department, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Roma, RM, ItalyCollege of Engineering and Technology, American University of the Middle East, KuwaitInstitute of Geosciences, Marine and Land Geomechanics and Geotectonics, Christian-Albrechts-Universität, Kiel, 24118, Germany; Corresponding author.Department of Chemical Engineering, McGill University, Montreal, QC, H3A 0C5, Canada; Co-corresponding authors.Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran; Co-corresponding authors.Enhancing efficiency and boosting output from oil reservoirs has consistently captured the attention of engineers and industrialists within the energy sector. In recent years, there has been a notable increase in the application of enhanced oil recovery (EOR) techniques. EOR methods refer to operations which are designed in order to maximize the oil recovery factor. Among various gas mixtures that are proposed as candidates to be injected into mature oil reservoirs, CO2 gas attains miscibility with the resident hydrocarbon fluid at a reasonable pressure and increases the oil recovery factor. CO2 injection, as an EOR method, has the potential of being coupled with CO2 sequestration and reducing the emission of greenhouse gas. To design a successful CO2 injection process, it is very important to have precise knowledge about the compressibility factor (Z-factor) of CO2 as it directly affects material balance calculations, pipeline design, design of surface facilities, and CO2 compression. Z-factor, also defined as the gas deviation factor, is mathematically explained as the ratio of actual gas volume to that of an ideal gas at a given temperature and pressure. In this study, two powerful and robust tree-based machine learning (ML) algorithms, namely light gradient boosted machine (LightGBM) and extreme gradient boosting (XGBoost) were utilized to precisely estimate CO2 Z-factor. To this end, a comprehensive databank from the literature is employed, which contains 2118 data points over extensive ranges of pressures and temperatures. The proposed models predict the CO2 Z-factor with respect to reduced temperature (Tr) and reduced pressure (Pr). Moreover, the results of the developed techniques were compared with those of Patel-Teja (PT) and Peng-Robinson (PR) equations of state (EoSs) applying various graphical and statistical error tests. The results demonstrated that the LightGBM intelligent technique has the highest accuracy with the lowest error value of 0.42 % and R2 of 0.999. The trend analysis illustrated that the LightGBM model could verify the actual variation of CO2 Z-factor with pressure (direct relationship) and accurately forecast the physical behavior of the CO2 Z-factor variation. Lately, outlier detection utilizing the Leverage approach illustrated that nearly all data points, except only 39 points, were statistically reliable and located in the valid zone. The results of this research can extremely help for better understanding of CO2 sequestration, decreasing the greenhouse gas emission, and exploring EOR techniques especially CO2 injection.http://www.sciencedirect.com/science/article/pii/S2590123025001239CO2Compressibility factorXGBoostLightGBMEORLeverage approach |
| spellingShingle | Behnam Amiri-Ramsheh Aydin Larestani Saeid Atashrouz Elnaz Nasirzadeh Meriem Essakhraoui Ali Abedi Mehdi Ostadhassan Ahmad Mohaddespour Abdolhossein Hemmati-Sarapardeh Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state Results in Engineering CO2 Compressibility factor XGBoost LightGBM EOR Leverage approach |
| title | Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state |
| title_full | Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state |
| title_fullStr | Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state |
| title_full_unstemmed | Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state |
| title_short | Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state |
| title_sort | toward accurate prediction of carbon dioxide co2 compressibility factor using tree based intelligent schemes xgboost and lightgbm and equations of state |
| topic | CO2 Compressibility factor XGBoost LightGBM EOR Leverage approach |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025001239 |
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