Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods
Abstract In recent decades, Enhanced Oil Recovery (EOR) has emerged as a primary method to increase reservoir oil recovery rates. One of these methods involves injecting miscible and immiscible gases. In miscible gas injection, the minimum miscibility pressure (MMP) is crucial, representing the crit...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-02-01
|
| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13202-024-01915-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823863253456912384 |
|---|---|
| author | Ali Akbari Ali Ranjbar Yousef Kazemzadeh Fatemeh Mohammadinia Amirjavad Borhani |
| author_facet | Ali Akbari Ali Ranjbar Yousef Kazemzadeh Fatemeh Mohammadinia Amirjavad Borhani |
| author_sort | Ali Akbari |
| collection | DOAJ |
| description | Abstract In recent decades, Enhanced Oil Recovery (EOR) has emerged as a primary method to increase reservoir oil recovery rates. One of these methods involves injecting miscible and immiscible gases. In miscible gas injection, the minimum miscibility pressure (MMP) is crucial, representing the critical pressure at which these gases can mix effectively with the oil phase. However, accurately determining the minimum pressure required for CO2 to miscible combine with the oil phase has always been a significant challenge. Various methods, including slim-tube tests, analytical models, and empirical correlations, are employed to determine MMP. Nevertheless, experimental measurements are time-consuming and costly. At the same time, mathematical models may yield different estimations. This study introduces an innovative approach using machine learning (ML) techniques to determine CO2-MMP during CO2 flooding. These methods produce reliable models, and advanced CO2-MMP techniques have demonstrated improved performance, significantly reducing time and costs. Furthermore, ML algorithms such as Artificial Neural Networks (ANN), Bayesian networks, Random Forest (RF), Support Vector Machine (SVM), LSBoost, and Linear Regression (LR) were employed to estimate MMP. Input data for these algorithms include CO2, H2S, N2, C1, C2, C3, C4, C5, C6, C7+, MWC5+, MWC7+, T, alongside vol/int. Comparative analysis with experimental MMP data revealed that the Glaso method achieves an accuracy of 0.8749, among the most precise methods, while SVM performed best among the mentioned ML algorithms with an accuracy of 0.986 and RMSE of 0.027. |
| format | Article |
| id | doaj-art-26734121197e41448383f94358f76f7a |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-26734121197e41448383f94358f76f7a2025-02-09T12:13:38ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-02-0115212410.1007/s13202-024-01915-3Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methodsAli Akbari0Ali Ranjbar1Yousef Kazemzadeh2Fatemeh Mohammadinia3Amirjavad Borhani4Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf UniversityAbstract In recent decades, Enhanced Oil Recovery (EOR) has emerged as a primary method to increase reservoir oil recovery rates. One of these methods involves injecting miscible and immiscible gases. In miscible gas injection, the minimum miscibility pressure (MMP) is crucial, representing the critical pressure at which these gases can mix effectively with the oil phase. However, accurately determining the minimum pressure required for CO2 to miscible combine with the oil phase has always been a significant challenge. Various methods, including slim-tube tests, analytical models, and empirical correlations, are employed to determine MMP. Nevertheless, experimental measurements are time-consuming and costly. At the same time, mathematical models may yield different estimations. This study introduces an innovative approach using machine learning (ML) techniques to determine CO2-MMP during CO2 flooding. These methods produce reliable models, and advanced CO2-MMP techniques have demonstrated improved performance, significantly reducing time and costs. Furthermore, ML algorithms such as Artificial Neural Networks (ANN), Bayesian networks, Random Forest (RF), Support Vector Machine (SVM), LSBoost, and Linear Regression (LR) were employed to estimate MMP. Input data for these algorithms include CO2, H2S, N2, C1, C2, C3, C4, C5, C6, C7+, MWC5+, MWC7+, T, alongside vol/int. Comparative analysis with experimental MMP data revealed that the Glaso method achieves an accuracy of 0.8749, among the most precise methods, while SVM performed best among the mentioned ML algorithms with an accuracy of 0.986 and RMSE of 0.027.https://doi.org/10.1007/s13202-024-01915-3Gas injectionMiscible pressure (MMP)Carbon dioxide gas (CO2)Experimental methodsMachine learning (ML) |
| spellingShingle | Ali Akbari Ali Ranjbar Yousef Kazemzadeh Fatemeh Mohammadinia Amirjavad Borhani Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods Journal of Petroleum Exploration and Production Technology Gas injection Miscible pressure (MMP) Carbon dioxide gas (CO2) Experimental methods Machine learning (ML) |
| title | Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods |
| title_full | Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods |
| title_fullStr | Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods |
| title_full_unstemmed | Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods |
| title_short | Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods |
| title_sort | estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods |
| topic | Gas injection Miscible pressure (MMP) Carbon dioxide gas (CO2) Experimental methods Machine learning (ML) |
| url | https://doi.org/10.1007/s13202-024-01915-3 |
| work_keys_str_mv | AT aliakbari estimationofminimummisciblepressureincarbondioxidegasinjectionusingmachinelearningmethods AT aliranjbar estimationofminimummisciblepressureincarbondioxidegasinjectionusingmachinelearningmethods AT yousefkazemzadeh estimationofminimummisciblepressureincarbondioxidegasinjectionusingmachinelearningmethods AT fatemehmohammadinia estimationofminimummisciblepressureincarbondioxidegasinjectionusingmachinelearningmethods AT amirjavadborhani estimationofminimummisciblepressureincarbondioxidegasinjectionusingmachinelearningmethods |