Article cité par

La fonctionnalité Article cité par… liste les citations d'un article. Ces citations proviennent de la base de données des articles de EDP Sciences, ainsi que des bases de données d'autres éditeurs participant au programme CrossRef Cited-by Linking Program. Vous pouvez définir une alerte courriel pour être prévenu de la parution d'un nouvel article citant " cet article (voir sur la page du résumé de l'article le menu à droite).

Article cité :

Cost-Effective Strategies for Assessing CO2 Water-Alternating-Gas (WAG) Injection for Enhanced Oil Recovery (EOR) in a Heterogeneous Reservoir

Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Dung Bui, Jonathan Asante, Irma Primasari, Adewale Amosu, Son Nguyen, Samuel Appiah Acheampong, Anthony Hama, William Ampomah and Angus Eastwood-Anaba
Water 17 (5) 651 (2025)
https://doi.org/10.3390/w17050651

Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure

Saad Alatefi, Okorie Ekwe Agwu, Reda Abdel Azim, Ahmad Alkouh and Iskandar Dzulkarnain
Chemical Engineering Research and Design 205 672 (2024)
https://doi.org/10.1016/j.cherd.2024.04.033

Research and application of EOR by gas injection in deep clastic reservoir: case study on Tazhong 402 CIII reservoir in Tarim Oilfield

Yongqiang Xu, Rujun Wang, Lunjie Chang, Daiyu Zhou, Zangyuan Wu, Gengping Yan, Guangqiang Shao and Yang Li
Petroleum Science and Technology 42 (13) 1638 (2024)
https://doi.org/10.1080/10916466.2022.2149796

On the Evaluation of Coal Strength Alteration Induced by CO2 Injection Using Advanced Black-Box and White-Box Machine Learning Algorithms

Qichao Lv, Haimin Zheng, Xiaochen Li, Mohammad-Reza Mohammadi, Fahimeh Hadavimoghaddam, Tongke Zhou, Atena Mahmoudzadeh and Abdolhossein Hemmati-Sarapardeh
SPE Journal 29 (03) 1672 (2024)
https://doi.org/10.2118/218403-PA

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

Menad Nait Amar, Hakim Djema, Khaled Ourabah, Fahd Mohamad Alqahtani and Mohammad Ghasemi
Energy & Fuels 38 (16) 14891 (2024)
https://doi.org/10.1021/acs.energyfuels.4c02249

Toward smart correlations for predicting in-situ stress: Application to evaluating subsurface energy structures

Fahimeh Hadavimoghaddam, Aboozar Garavand, Alexei Rozhenko, Masoud Mostajeran Gortani and Abdolhossein Hemmati-Sarapardeh
Geoenergy Science and Engineering 231 212292 (2023)
https://doi.org/10.1016/j.geoen.2023.212292

Physics guided data-driven model to estimate minimum miscibility pressure (MMP) for hydrocarbon gases

Utkarsh Sinha, Birol Dindoruk and Mohamed Soliman
Geoenergy Science and Engineering 224 211389 (2023)
https://doi.org/10.1016/j.geoen.2022.211389

Exploring the power of machine learning in analyzing the gas minimum miscibility pressure in hydrocarbons

Mahsheed Rayhani, Afshin Tatar, Amin Shokrollahi and Abbas Zeinijahromi
Geoenergy Science and Engineering 226 211778 (2023)
https://doi.org/10.1016/j.geoen.2023.211778

A novel correlation for modeling interfacial tension in binary mixtures of CH₄, CO₂, and N₂ + normal alkanes systems: Application to gas injection EOR process

Reza Behvandi and Mohsen Mirzaie
Fuel 325 124622 (2022)
https://doi.org/10.1016/j.fuel.2022.124622

Direct Measurement of Minimum Miscibility Pressure of Decane and CO2 in Nanoconfined Channels

Bo Bao, Jia Feng, Junjie Qiu and Shuangliang Zhao
ACS Omega 6 (1) 943 (2021)
https://doi.org/10.1021/acsomega.0c05584

Research for reducing the Minimum Miscible Pressure of crude oil and carbon dioxide by injecting citric acid isobutyl ester

Guangjuan Fan, Yuejun Zhao, Yilin Li, Xiaodan Zhang and Hao Chen
Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 76 30 (2021)
https://doi.org/10.2516/ogst/2021007

Applications of Artificial Intelligence Techniques in the Petroleum Industry

Abdolhossein Hemmati-Sarapardeh, Aydin Larestani, Menad Nait Amar and Sassan Hajirezaie
Applications of Artificial Intelligence Techniques in the Petroleum Industry 79 (2020)
https://doi.org/10.1016/B978-0-12-818680-0.00004-7