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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:

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

Showing 1-25 of 40 citing articles:

Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin
Shengjie Jiao, Wei Li, Zhuolun Li, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 21
Profiling of weathered argillaceous limestone rock with MWD data from advanced drilling for tunnelling along Wu-Kai expressway in Chongqing, China
Siyuan Wu, Wendal Victor Yue, Min Qiu, et al.
Tunnelling and Underground Space Technology (2024) Vol. 147, pp. 105719-105719
Closed Access | Times Cited: 8
Research on adaptive prediction model of rate of penetration under dynamic formation conditions
Hu Yin, Xiuwen Zhao, Qian Li
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108281-108281
Closed Access | Times Cited: 6
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
A Review of AI Applications in Unconventional Oil and Gas Exploration and Development
Feiyu Chen, Linghui Sun, Siyu Jian, et al.
Energies (2025) Vol. 18, Iss. 2, pp. 391-391
Open Access
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
Intelligent Prediction of Rate of Penetration through Meta-Learning and Data Augmentation Synergy under Limited Sample
Zhengchao Ma, Jintao Weng, Junkai Zhang, et al.
Geoenergy Science and Engineering (2025), pp. 213818-213818
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
In situ strength profiles along two adjacent vertical drillholes from digitalization of hydraulic rotary drilling
Xuefan Wang, Peng Peng, Zhigang Shan, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 15, Iss. 1, pp. 146-168
Open Access | Times Cited: 19
Reservoir production optimization based on surrograte model and differential evolution algorithm
Jianwei Gu, Wei Liu, Kai Zhang, et al.
Journal of Petroleum Science and Engineering (2021) Vol. 205, pp. 108879-108879
Closed Access | Times Cited: 26
Analysis and Multi-Objective Optimization of the Rate of Penetration and Mechanical Specific Energy: A Case Study Applied to a Carbonate Hard Rock Reservoir Based on a Drill Rate Test Using Play-Back Methodology
Diunay Zuliani Mantegazini, Andreas Nascimento, Vitória Felício Dornelas, et al.
Applied Sciences (2024) Vol. 14, Iss. 6, pp. 2234-2234
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
Research on adaptive feature optimization and drilling rate prediction based on real-time data
Jun Ren, Jie Jiang, Changchun Zhou, et al.
Geoenergy Science and Engineering (2024) Vol. 242, pp. 213247-213247
Closed Access | Times Cited: 3
Prediction of rate of penetration in directional drilling using data mining techniques
Kaveh Shaygan, Saeid Jamshidi
Geoenergy Science and Engineering (2022) Vol. 221, pp. 111293-111293
Closed Access | Times Cited: 15
A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering
Mitra Khalilidermani, Dariusz Knez
Energies (2023) Vol. 16, Iss. 11, pp. 4289-4289
Open Access | Times Cited: 8
Application of mathematical and machine learning models to predict differential pressure of autonomous downhole inflow control devices
Hossein Yavari, Rasool Khosravanian, David A. Wood, et al.
ADVANCES IN GEO-ENERGY RESEARCH (2021) Vol. 5, Iss. 4, pp. 386-406
Open Access | Times Cited: 18
A case study of drilling process monitoring for geomaterial strength assessment along hydraulic rotary drillhole
X. F. Wang, Ping Peng, Wenhui Yue, et al.
Bulletin of Engineering Geology and the Environment (2023) Vol. 82, Iss. 8
Closed Access | Times Cited: 7
A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks
Danial Etesami, Wenjun Zhang, Mohsen Hadian
Journal of Natural Gas Science and Engineering (2021) Vol. 95, pp. 104104-104104
Closed Access | Times Cited: 16
Real-time porosity prediction using gas-while-drilling data and machine learning with reservoir associated gas: Case study for Hassi Messaoud field, Algeria
Ouafi Ameur-Zaimeche, Rabah Kechiched, Salim Heddam, et al.
Marine and Petroleum Geology (2022) Vol. 140, pp. 105631-105631
Closed Access | Times Cited: 11
Determining the difference of kick tolerance with single bubble and dynamic multiphase models: Evaluation of well-control with water/synthetic based muds
Javed Akbar Khan, Sonny Irawan, Iliyas Bin Md Dan, et al.
Ain Shams Engineering Journal (2022) Vol. 13, Iss. 4, pp. 101678-101678
Open Access | Times Cited: 9
Wide and deep cross network for the rate of penetration prediction
Zhi‐Jun Pei, Xianzhi Song, Yong Ji, et al.
Geoenergy Science and Engineering (2023) Vol. 229, pp. 212066-212066
Closed Access | Times Cited: 5
Evaluating the structure and mechanical properties of deep rock masses based on drilling process monitoring and borehole televiewer
Shaojun Li, Huaisheng Xu, Minzong Zheng, et al.
Bulletin of Engineering Geology and the Environment (2023) Vol. 82, Iss. 10
Closed Access | Times Cited: 5
On drilling speed of London Clay from MWD data with time-series algorithm for ground characterisation
Si Wu, Wendal Victor Yue, Zhongqi Yue
Géotechnique (2023) Vol. 75, Iss. 2, pp. 166-179
Closed Access | Times Cited: 5
Transforming Oil Well Drilling: Prediction of Real-Time Rate of Penetration with Novel Machine Learning Approach in Varied Lithological Formations
Raunak Gupta, Uttam K. Bhui
Journal of energy resources technology. (2024) Vol. 1, Iss. 1
Closed Access | Times Cited: 1
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