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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:

Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: A case study on the Shennongjia area, Central China
Chao Gan, Weihua Cao, Min Wu, et al.
Journal of Petroleum Science and Engineering (2019) Vol. 181, pp. 106200-106200
Closed Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Developing a new rigorous drilling rate prediction model using a machine learning technique
Mohammad Mehrad, Mahdi Bajolvand, Ahmad Ramezanzadeh, et al.
Journal of Petroleum Science and Engineering (2020) Vol. 192, pp. 107338-107338
Closed Access | Times Cited: 66
Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models
Ehsan Brenjkar, Ebrahim Biniaz Delijani
Journal of Petroleum Science and Engineering (2021) Vol. 210, pp. 110033-110033
Closed Access | Times Cited: 42
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: 16
Prediction of rate of penetration based on drilling conditions identification for drilling process
Xiao Yang, Min Wu, Chengda Lu, et al.
Neurocomputing (2024) Vol. 579, pp. 127439-127439
Closed Access | Times Cited: 7
Research on a drilling rate of penetration prediction model based on the improved chaos whale optimization and back propagation algorithm
Kanhua Su, Wenhao Da, Meng Li, et al.
Geoenergy Science and Engineering (2024) Vol. 240, pp. 213017-213017
Closed Access | Times Cited: 7
Hybrid data driven drilling and rate of penetration optimization
Ammar Alali, Mahmoud Abughaban, Beshir Aman, et al.
Journal of Petroleum Science and Engineering (2020) Vol. 200, pp. 108075-108075
Open Access | Times Cited: 46
A New Hybrid Bat Algorithm and its Application to the ROP Optimization in Drilling Processes
Chao Gan, Weihua Cao, Kang‐Zhi Liu, et al.
IEEE Transactions on Industrial Informatics (2019) Vol. 16, Iss. 12, pp. 7338-7348
Closed Access | Times Cited: 44
Real-time prediction of rate of penetration while drilling complex lithologies using artificial intelligence techniques
Salaheldin Elkatatny
Ain Shams Engineering Journal (2020) Vol. 12, Iss. 1, pp. 917-926
Open Access | Times Cited: 40
A novel rate of penetration prediction model with identified condition for the complex geological drilling process
Yang Zhou, Xin Chen, Haibin Zhao, et al.
Journal of Process Control (2021) Vol. 100, pp. 30-40
Closed Access | Times Cited: 34
Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms - A review
Mohammad Najjarpour, Hossein Jalalifar, Saeid Norouzi-Apourvari
Journal of Petroleum Science and Engineering (2021) Vol. 208, pp. 109575-109575
Closed Access | Times Cited: 33
Modelling rate of penetration in drilling operations using RBF, MLP, LSSVM, and DT models
M.R. Riazi, Hossein Mehrjoo, Reza Nakhaei, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 25
Study on rock-breaking mechanism and bit adaptive characteristics under the action of claw-toe impregnated diamond bit
Yan Zhao, Yumin Wen, Ke Gao, et al.
Geoenergy Science and Engineering (2024) Vol. 236, pp. 212752-212752
Closed Access | Times Cited: 5
Real-time monitoring and optimization of drilling performance using artificial intelligence techniques: a review
David A. Wood
Elsevier eBooks (2024), pp. 169-210
Closed Access | Times Cited: 4
Analyzing machine learning algorithms in predicting Ranikot swelling at different compaction pressures in presence of carbon supported TiO2 water based mud
Faiq Azhar Abbasi, Syed Mohammad Ali Shah, Muhammad Mustafa, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
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
Research on the application of particle swarm optimization algorithm based on heterogeneous lithology analysis and drilling parameter correction to optimize the back propagation neural network rate of penetration prediction model
Xinjie Fang, Zhongzhi Hu, Song Wei, et al.
Petroleum Science and Technology (2025), pp. 1-22
Closed Access
Developing a New Model for Drilling Rate of Penetration Prediction Using Convolutional Neural Network
Morteza Matinkia, Amirhossein Sheykhinasab, Soroush Shojaei, et al.
Arabian Journal for Science and Engineering (2022) Vol. 47, Iss. 9, pp. 11953-11985
Closed Access | Times Cited: 20
An event-triggered approach to torsional vibration control of drill-string system using measurement-while-drilling data
Chengda Lu, Min Wu, Luefeng Chen, et al.
Control Engineering Practice (2020) Vol. 106, pp. 104668-104668
Closed Access | Times Cited: 29
A novel dynamic model for the online prediction of rate of penetration and its industrial application to a drilling process
Chao Gan, Weihua Cao, Kang‐Zhi Liu, et al.
Journal of Process Control (2021) Vol. 109, pp. 83-92
Closed Access | Times Cited: 25
Predicting Rate of Penetration of Horizontal Drilling by Combining Physical Model with Machine Learning Method in the China Jimusar Oil Field
Chuanjie Ren, Wenjun Huang, Deli Gao
SPE Journal (2022) Vol. 28, Iss. 06, pp. 2713-2736
Closed Access | Times Cited: 17
An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)
Hui Ji, Yishan Lou, Shuting Cheng, et al.
ACS Omega (2022) Vol. 8, Iss. 1, pp. 934-945
Open Access | Times Cited: 16
An approach for optimization of controllable drilling parameters for motorized bottom hole assembly in a specific formation
Hossein Yavari, Mohammad Fazaelizadeh, Bernt S. Aadnøy, et al.
Results in Engineering (2023) Vol. 20, pp. 101548-101548
Open Access | Times Cited: 9
Rate of penetration prediction with uncertainty assessment: Case study of a middle-east oil field
Reza Jalakani, Seyyed Shahab Tabatabaee Moradi
Results in Engineering (2024) Vol. 21, pp. 101793-101793
Open Access | Times Cited: 3
A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade
Qian Li, Junping Li, Lan-Lan Xie
Petroleum Science (2024) Vol. 21, Iss. 5, pp. 3496-3516
Open Access | Times Cited: 3
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