Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery
Abstract Given the escalating concerns surrounding climate change and global warming, reducing anthropogenic carbon dioxide emissions has become more urgent than ever. CO2 enhanced oil recovery emerges as a promising scenario for growing carbon capture, utilization, and storage efforts. However, the...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-024-01900-w |
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| author | Milad Asghari Sajjad Moslehi Mohammad Emami Niri Shahin Kord |
| author_facet | Milad Asghari Sajjad Moslehi Mohammad Emami Niri Shahin Kord |
| author_sort | Milad Asghari |
| collection | DOAJ |
| description | Abstract Given the escalating concerns surrounding climate change and global warming, reducing anthropogenic carbon dioxide emissions has become more urgent than ever. CO2 enhanced oil recovery emerges as a promising scenario for growing carbon capture, utilization, and storage efforts. However, the success of CO2 injection is highly dependent on the distinctive characteristics of reservoirs. Within this context, the minimum miscibility pressure is a pivotal parameter for effectively screening and evaluating the viability of CO2 flooding projects. Previous studies have proposed a range of models to estimate the minimum miscibility pressure. However, these models need to be revised to meet the desired levels of accuracy. Similarly, machine learning methods have been explored, given the advancements achieved in previous research. However, the inherent black-box nature of these approaches may limit their practical applicability. The current study introduces an innovative grey-box machine learning modeling method to bridge this gap. Through this approach, precise and user-friendly equations are developed to facilitate accurate minimum miscibility pressure estimation. The investigation reveals that grey-box methods yield remarkable levels of accuracy and are highly suitable for precise minimum miscibility pressure estimation. The findings of this study highlight the potential of grey-box machine learning modeling as a valuable tool in the field of CO2 flooding and contribute to the ongoing endeavors in carbon capture, utilization, and storage. |
| format | Article |
| id | doaj-art-8e073817abcd4b9ab860a6399cff5a87 |
| institution | DOAJ |
| 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-8e073817abcd4b9ab860a6399cff5a872025-08-20T02:51:27ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-02-0115312310.1007/s13202-024-01900-wPrediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recoveryMilad Asghari0Sajjad Moslehi1Mohammad Emami Niri2Shahin Kord3Institute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of TehranDepartment of Petroleum Engineering, Ahvaz Faculty of Petroleum, Petroleum University of TechnologyInstitute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of TehranDepartment of Petroleum Engineering, Ahvaz Faculty of Petroleum, Petroleum University of TechnologyAbstract Given the escalating concerns surrounding climate change and global warming, reducing anthropogenic carbon dioxide emissions has become more urgent than ever. CO2 enhanced oil recovery emerges as a promising scenario for growing carbon capture, utilization, and storage efforts. However, the success of CO2 injection is highly dependent on the distinctive characteristics of reservoirs. Within this context, the minimum miscibility pressure is a pivotal parameter for effectively screening and evaluating the viability of CO2 flooding projects. Previous studies have proposed a range of models to estimate the minimum miscibility pressure. However, these models need to be revised to meet the desired levels of accuracy. Similarly, machine learning methods have been explored, given the advancements achieved in previous research. However, the inherent black-box nature of these approaches may limit their practical applicability. The current study introduces an innovative grey-box machine learning modeling method to bridge this gap. Through this approach, precise and user-friendly equations are developed to facilitate accurate minimum miscibility pressure estimation. The investigation reveals that grey-box methods yield remarkable levels of accuracy and are highly suitable for precise minimum miscibility pressure estimation. The findings of this study highlight the potential of grey-box machine learning modeling as a valuable tool in the field of CO2 flooding and contribute to the ongoing endeavors in carbon capture, utilization, and storage.https://doi.org/10.1007/s13202-024-01900-wMachine learningMinimum miscible pressureGrey-boxEnhanced oil recoveryCarbon capture, utilization and storage |
| spellingShingle | Milad Asghari Sajjad Moslehi Mohammad Emami Niri Shahin Kord Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery Journal of Petroleum Exploration and Production Technology Machine learning Minimum miscible pressure Grey-box Enhanced oil recovery Carbon capture, utilization and storage |
| title | Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery |
| title_full | Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery |
| title_fullStr | Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery |
| title_full_unstemmed | Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery |
| title_short | Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery |
| title_sort | prediction of minimum miscibility pressure of co2 oil systems using grey box modeling for carbon dioxide capture utilization storage and enhanced oil recovery |
| topic | Machine learning Minimum miscible pressure Grey-box Enhanced oil recovery Carbon capture, utilization and storage |
| url | https://doi.org/10.1007/s13202-024-01900-w |
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