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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:

Comparison of Machine Learning Algorithms for Sand Production Prediction: An Example for a Gas-Hydrate-Bearing Sand Case
Jinze Song, Yuhao Li, Shuai Liu, et al.
Energies (2022) Vol. 15, Iss. 18, pp. 6509-6509
Open Access | Times Cited: 10

Showing 10 citing articles:

Interpretable Machine Learning Method for Compressive Strength Prediction and Analysis of Pure Fly Ash-based Geopolymer Concrete
Shi Yuqiong, Jingyi Li, Yang Zhang, et al.
Journal of Wuhan University of Technology-Mater Sci Ed (2025) Vol. 40, Iss. 1, pp. 65-78
Closed Access
Artificial intelligence in geoenergy: bridging petroleum engineering and future-oriented applications
Sungil Kim, Tea-Woo Kim, Suryeom Jo
Journal of Petroleum Exploration and Production Technology (2025) Vol. 15, Iss. 2
Open Access
Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring
Dejen Teklu Asfha, Abdul Halim Abdul Latiff, Daniel Asante Otchere, et al.
Journal of Petroleum Exploration and Production Technology (2024) Vol. 14, Iss. 10, pp. 2577-2616
Open Access | Times Cited: 2
Unveiling the potential of CO2 hydrates in porous media: A review on kinetic modelling, molecular dynamics simulations, and machine learning
Amirun Nissa Rehman, Cornelius B. Bavoh, Mohd Yusuf Khan, et al.
Fuel (2024) Vol. 381, pp. 133650-133650
Closed Access | Times Cited: 1
Use of Neural Networks for Lifetime Analysis of Teeming Ladles
Dalibor Jančar, Mario MACHŮ, Marek Velička, et al.
Materials (2022) Vol. 15, Iss. 22, pp. 8234-8234
Open Access | Times Cited: 8
Application of Machine Learning to Predict Transient Sand Production in the Karazhanbas Oil Field, Ustyurt–Buzachi Basin (West Kazakhstan)
Ainash Shabdirova, Ashirgul Kozhagulova, Nguyen Hop Minh, et al.
Natural Resources Research (2023) Vol. 32, Iss. 5, pp. 1975-1986
Closed Access | Times Cited: 3
Sand Production Prediction with Machine Learning using Input Variables from Geological and Operational Conditions in the Karazhanbas Oilfield, Kazakhstan
Ainash Shabdirova, Ashirgul Kozhagulova, Yernazar Samenov, et al.
Natural Resources Research (2024) Vol. 33, Iss. 6, pp. 2789-2805
Open Access
Method and application of sand body thickness prediction based on virtual sample machine learning
Yan Zhen, Zhen Zhao, Xiaoming Zhao, et al.
Geophysics (2024) Vol. 89, Iss. 6, pp. M169-M184
Closed Access
Advanced Prediction of Sand Production Onset Using Data Analysis and Machine Learning Techniques: Insights from Adriatic Sea Gas Wells
Scott V. Nguyen, Mohamed Adel Gabry, Mohamed Y. Soliman, et al.
(2024)
Closed Access
Machine Learning-Based Research on Reserve Prediction of Natural-Gas-Hydrates
S. Ye, Shuxian Mai, Wenbo Niu, et al.
(2023), pp. 53-58
Closed Access
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