Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

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

Showing 1-25 of 42 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
Experimental investigation and intelligent modeling of pore structure changes in type III kerogen-rich shale artificially matured by hydrous and anhydrous pyrolysis
Bo Liu, Mohammad-Reza Mohammadi, Zhongliang Ma, et al.
Energy (2023) Vol. 282, pp. 128799-128799
Closed Access | Times Cited: 20
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
Predicting Rate of Penetration in Ultra-deep Wells Based on Deep Learning Method
Chi Peng, Jianyun Pang, Jianhong Fu, et al.
Arabian Journal for Science and Engineering (2023) Vol. 48, Iss. 12, pp. 16753-16768
Closed Access | Times Cited: 15
Enhancing rate of penetration prediction in drilling operations: A data stream framework approach
João Roberto Bertini, Bahram Lavi
Engineering Applications of Artificial Intelligence (2025) Vol. 143, pp. 110034-110034
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
A Novel Approach for the Prediction of Real-Time Rate of Penetration in Drilling for Petroleum by Combining the Attention-Based Bidirectional-Long Short-Term Memory and Long Short-Term Memory
Zhuxi Lyu, Wei Fang, Yi Pan, et al.
International Petroleum Technology Conference (2025)
Closed Access
Research on the mechanism of high-efficiency milling cement plugs with low weight on bit and high rotational speed
Zirui Li, Feng Guan, Feng Wan, et al.
Journal of Petroleum Exploration and Production Technology (2025) Vol. 15, Iss. 3
Open Access
A real-time prediction method for rate of penetration sequence in offshore deep wells drilling based on attention mechanism-enhanced BiLSTM model
Qi Yuan, Miao He, Zhichao Chen, et al.
Ocean Engineering (2025) Vol. 325, pp. 120820-120820
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
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
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
Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site
Mohamed Arbi Ben Aoun, Tamás Madarász
Energies (2022) Vol. 15, Iss. 12, pp. 4288-4288
Open Access | Times Cited: 18
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
Developing GAN-boosted Artificial Neural Networks to model the rate of drilling bit penetration
Mohammad Hassan Sharifinasab, Mohammad Emami Niri, Milad Masroor
Applied Soft Computing (2023) Vol. 136, pp. 110067-110067
Closed Access | Times Cited: 10
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
Fusion model of weight on bit in horizontal exploration hole based on wavelet transform and machine learning
Xikun Gao, Dajun Zhao, Yan Zhao, et al.
Geoenergy Science and Engineering (2024) Vol. 234, pp. 212654-212654
Closed Access | Times Cited: 3
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
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
Drilling operation optimization using machine learning framework
Mohammad Eltrissi, Omar Yousef, Ahmed H. El-Banbi, et al.
Geoenergy Science and Engineering (2023) Vol. 228, pp. 211969-211969
Closed Access | Times Cited: 8
Measurement and prediction of energy consumption of rig-operator system based on digital drilling technology
Kangping Gao, Qian Zhang, Shengjie Jiao, et al.
Measurement (2024) Vol. 239, pp. 115468-115468
Closed Access | Times Cited: 2
Automated neural network optimization for data-driven predictive models: an application to ROP in drilling
Imene Khebouri, Saïd Rechak, Ihab Abderraouf Boulham, et al.
Soft Computing (2024)
Closed Access | Times Cited: 2
Page 1 - Next Page

Scroll to top