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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Novel Way to Predict the MMP of a CO2–Oil System Using Stacking Models
Liu Bing-guan, Zhilin Wang, Yong Jin, et al.
Energy & Fuels (2023) Vol. 37, Iss. 2, pp. 935-944
Closed Access | Times Cited: 9

Showing 9 citing articles:

Multiphase fluid-rock interactions and flow behaviors in shale nanopores: A comprehensive review
Jianchao Cai, Xinghe Jiao, Han Wang, et al.
Earth-Science Reviews (2024) Vol. 257, pp. 104884-104884
Closed Access | Times Cited: 41
Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure
Saad Alatefi, Okorie E. Agwu, Reda Abdel Azim, et al.
Process Safety and Environmental Protection (2024) Vol. 205, pp. 672-694
Closed Access | Times Cited: 11
Comprehensive Review of the Determination and Reduction of the Minimum Miscibility Pressure during CO2 Flooding
Guoliang Song, Y. Gloria Meng, Chengli Zhang, et al.
ACS Omega (2024) Vol. 9, Iss. 13, pp. 14747-14765
Open Access | Times Cited: 10
Leveraging machine learning in porous media
Mostafa Delpisheh, Benyamin Ebrahimpour, Abolfazl Fattahi, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 32, pp. 20717-20782
Open Access | Times Cited: 9
Determination of Gas–Oil minimum miscibility pressure for impure CO2 through optimized machine learning models
Chenyu Wu, Lu Jin, Jin Zhao, et al.
Geoenergy Science and Engineering (2024) Vol. 242, pp. 213216-213216
Closed Access | Times Cited: 6
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, et al.
Energy & Fuels (2024) Vol. 38, Iss. 16, pp. 14891-14924
Closed Access | Times Cited: 5
A comprehensive review of minimum miscibility pressure determination and reduction strategies between CO2 and crude oil in CCUS processes
Zhuo Chen, Jiahui Chen, Xin Zhang
Fuel (2024) Vol. 384, pp. 134053-134053
Closed Access | Times Cited: 4
Distribution Characteristics and Backflow Flow Pattern of a Slickwater Fracturing Fluid in Shale Based on Low-Field Nuclear Magnetic Resonance
Jiawei Liu, Jian Zhang, Yang Yue, et al.
Energy & Fuels (2024) Vol. 38, Iss. 3, pp. 2001-2009
Closed Access | Times Cited: 3
Optimizing Minimum Miscibility Pressure Prediction Using Machine Learning: A Comprehensive Evaluation and Validation
Oluwakemi Olofinnika, Anand Selveindran, Deep R. Patel, et al.
Energy & Fuels (2024) Vol. 38, Iss. 11, pp. 9365-9380
Open Access | Times Cited: 3
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