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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.

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Showing 21 citing articles:

Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage
Hemeng Zhang, Hung Vo Thanh, Mohammad Rahimi, et al.
The Science of The Total Environment (2023) Vol. 877, pp. 162944-162944
Closed Access | Times Cited: 45
Seasonal peak load prediction of underground gas storage using a novel two-stage model combining improved complete ensemble empirical mode decomposition and long short-term memory with a sparrow search algorithm
Weibiao Qiao, Zonghua Fu, Mingjun Du, et al.
Energy (2023) Vol. 274, pp. 127376-127376
Closed Access | Times Cited: 40
Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges
Shailesh Tripathi, Nadine Bachmann, Manuel Brunner, et al.
Journal Of Big Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 15
Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project
Majid Safaei-Farouji, Hung Vo Thanh, Zhenxue Dai, et al.
Journal of Cleaner Production (2022) Vol. 372, pp. 133778-133778
Closed Access | Times Cited: 62
Investigation of Wettability and IFT Alteration during Hydrogen Storage Using Machine Learning
Mehdi Maleki, Mohammad Rasool Dehghani, Ali Akbari, et al.
Heliyon (2024) Vol. 10, Iss. 19, pp. e38679-e38679
Open Access | Times Cited: 7
Data-Driven Machine Learning Models for Predicting Deliverability of Underground Natural Gas Storage in Aquifer and Depleted Reservoirs
Altaf Hussain, Peng–Zhi Pan, Javid Hussain, et al.
Energy (2025), pp. 134974-134974
Closed Access
An optimized neuro-fuzzy system using advance nature-inspired Aquila and Salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations
Mohammed A. A. Al‐qaness, Ahmed A. Ewees, Hung Vo Thanh, et al.
Journal of Energy Storage (2022) Vol. 56, pp. 106150-106150
Closed Access | Times Cited: 23
Long-term stability forecasting for energy storage salt caverns using deep learning-based model
Kai Zhao, Shinong Yu, Louis Ngai Yuen Wong
Energy (2025), pp. 134854-134854
Closed Access
Evaluating and Predicting Deliverability of Natural Gas Storage Sites Using Stacking Machine Learning Models
Wei Wei, Jian Hou, Xingqi Liu
Geoenergy Science and Engineering (2025), pp. 213771-213771
Closed Access
A robust hybrid near-real-time model for prediction of drilling fluids filtration
Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood, et al.
Engineering With Computers (2025)
Closed Access
Predicting water-based drilling fluid filtrate volume in close to real time from routine fluid property measurements
Shadfar Davoodi, Mohammed Ba Geri, David A. Wood, et al.
Petroleum (2025)
Open Access
Machine Learning In Sustainable Development–An Overview
Ramiz Salama, Fadi Al‐Turjman, Chadi Altrjman, et al.
(2023)
Closed Access | Times Cited: 11
Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM
Songlin Yang, Huiqing Lian, Mohamad Reza Soltanian, et al.
IEEE Transactions on Geoscience and Remote Sensing (2024) Vol. 62, pp. 1-12
Closed Access | Times Cited: 2
Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids
Shadfar Davoodi, Mohammed Murif Al-Rubaii, David A. Wood, et al.
Applied Sciences (2024) Vol. 14, Iss. 19, pp. 9035-9035
Open Access | Times Cited: 1
Optimization of a Typical Gas Injection Pressurization Process in Underground Gas Storage
Shuangqing Chen, Ze Yu, Yuchun Li, et al.
Sustainability (2024) Vol. 16, Iss. 20, pp. 8902-8902
Open Access | Times Cited: 1
An integrated intelligent approach to the determination of drilling fluids’ solid content
Shadfar Davoodi, Evgeny Burnaev, David A. Wood, et al.
Colloids and Surfaces A Physicochemical and Engineering Aspects (2024), pp. 135906-135906
Closed Access | Times Cited: 1
Underground Gas Storage Facilities in Ukraine: Current State and Future Prospects
Natalia Iwaszczuk, Ivanna Zapukhliak, Aleksander Iwaszczuk, et al.
Energies (2022) Vol. 15, Iss. 18, pp. 6604-6604
Open Access | Times Cited: 7
A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids
Rituraj Rituraj, Diana Ecker
(2022), pp. 000359-000368
Open Access | Times Cited: 3
Machine-Learning Predictive Model for Semiautomated Monitoring of Solid Content in Water-Based Drilling Fluids
Shadfar Davoodi, Sergey V. Muravyov, David A. Wood, et al.
Arabian Journal for Science and Engineering (2024)
Closed Access
Fuzzy, neural network, and expert system hybrid method development framework for cost contingency estimation in subsea gas pipeline construction
Muhammad Yusuf, Yusuf Latief
AIP conference proceedings (2024) Vol. 3219, pp. 080004-080004
Closed Access
A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids
Rituraj Rituraj, Diana Ecker
(2022)
Open Access
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