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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:

An explainable ensemble machine learning model to elucidate the influential drilling parameters based on rate of penetration prediction
Zhipeng Feng, Hamdan Gani, Annisa Dwi Damayanti, et al.
Geoenergy Science and Engineering (2023) Vol. 231, pp. 212231-212231
Closed Access | Times Cited: 15

Showing 15 citing articles:

Interpretable predictive modelling of outlet temperatures in Central Alberta’s hydrothermal system using boosting-based ensemble learning incorporating Shapley Additive exPlanations (SHAP) approach
Ruyang Yu, Kai Zhang, Tao Li, et al.
Energy (2025), pp. 134738-134738
Closed Access | Times Cited: 1
Simultaneously improving ROP and maintaining wellbore stability in shale gas well: A case study of Luzhou shale gas reservoirs
Yaoran Wei, Yongcun Feng, Zhenlai Tan, et al.
Rock Mechanics Bulletin (2024) Vol. 3, Iss. 3, pp. 100124-100124
Open Access | Times Cited: 10
Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China
Ruyang Yu, Kai Zhang, R. Brindha, et al.
Energy (2024) Vol. 296, pp. 131146-131146
Closed Access | Times Cited: 5
Optimization of Drilling Rate Based on Genetic Algorithms and Machine Learning Models
Fang Shi, Hualin Liao, Shuaishuai Wang, et al.
Geoenergy Science and Engineering (2025), pp. 213747-213747
Closed Access
Real-Time Inversion of Formation Drillability and Concurrent Speedup Strategies for Microdrilling Time Optimization
Huohai Yang, Zhirong Li, Lin Gao, et al.
SPE Journal (2025), pp. 1-16
Closed Access
Evaluating the Rate of Penetration With Deep‐Learning Predictive Models
C.F. Lee, Jongkook Kim, Namjoong Kim, et al.
International Journal of Energy Research (2025) Vol. 2025, Iss. 1
Open Access
Multidimensional Lost Circulation Risk Quantification Assessment Model Based on Ensemble Machine Learning
Haibo Mu, Guancheng Jiang, Wei Zhang, et al.
SPE Journal (2025), pp. 1-11
Closed Access
Explainable AI (XAI) Techniques for Convolutional Neural Network-Based Classification of Drilled Holes in Melamine Faced Chipboard
Alexander Sieradzki, Jakub Bednarek, Albina Jegorowa, et al.
Applied Sciences (2024) Vol. 14, Iss. 17, pp. 7462-7462
Open Access | Times Cited: 4
Data-driven framework for predicting rate of penetration in deepwater granitic formations: A marine engineering geology perspective with comprehensive model interpretability
Yichi Zhang, Liang Yu, Lele Yang, et al.
Engineering Geology (2025), pp. 108039-108039
Closed Access
A Comprehensive Study on the Optimization of Drilling Performance in Hybrid Nano-Composites and Neat CFRP Composites Using Statistical and Machine Learning Approaches
Tanzila Nargis, S. M. Shahabaz, Subash Acharya, et al.
Journal of Manufacturing and Materials Processing (2024) Vol. 8, Iss. 2, pp. 67-67
Open Access | Times Cited: 2
Research on a Drilling Rate of Penetration Prediction Model Based on the Icwoa-Bp Algorithm
Da Wenhao, Kanhua Su, Meng Li, et al.
(2024)
Closed Access | Times Cited: 1
A highly accurate and robust prediction framework for drilling rate of penetration based on machine learning ensemble algorithm
Yuxiang Yang, Xiao Cen, Haocheng Ni, et al.
Geoenergy Science and Engineering (2024), pp. 213423-213423
Closed Access | Times Cited: 1
Prediction of the ROP based on GA-LightGBM and drilling data
Shuai Shuai Wang, Jun Yan, Hao Geng
Geosystem Engineering (2024), pp. 1-19
Closed Access
Stop Using Black-Box Models: Application of Explainable Artificial Intelligence for Rate of Penetration Prediction
Han Meng, Botao Lin, Yan Jin
SPE Journal (2024), pp. 1-15
Closed Access
An online adaptive ROP prediction model using GBDT and Bayesian Optimization algorithm in drilling
Jiasheng Hao, Haomin Xu, Zhinan Peng, et al.
Geoenergy Science and Engineering (2024) Vol. 246, pp. 213596-213596
Closed Access
Enhanced Prediction of Rate of Penetration Using the Interpretable Hybrid Temporal Graph Neural Network
Rui Zhang, Zhaopeng Zhu, Xianzhi Song, et al.
(2024)
Closed Access
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