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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 26-50 of 74 citing articles:

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
Data-driven recurrent neural network model to predict the rate of penetration
Husam H. Alkinani, Abo Taleb T. Al-Hameedi, Shari Dunn‐Norman
Upstream Oil and Gas Technology (2021) Vol. 7, pp. 100047-100047
Closed Access | Times Cited: 22
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
Optimization of controllable drilling parameters using a novel geomechanics-based workflow
Mahdi Bajolvand, Ahmad Ramezanzadeh, Mohammad Mehrad, et al.
Journal of Petroleum Science and Engineering (2022) Vol. 218, pp. 111004-111004
Closed Access | Times Cited: 14
A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang
Naipeng Liu, Hui Gao, Zhen Zhao, et al.
Journal of Petroleum Exploration and Production Technology (2021) Vol. 12, Iss. 6, pp. 1595-1608
Open Access | Times Cited: 18
Prediction of Drilling Efficiency for Rotary Drilling Rig Based on an Improved Back Propagation Neural Network Algorithm
Cunde Jia, Junyong Zhang, Xiangdong Kong, et al.
Machines (2024) Vol. 12, Iss. 7, pp. 438-438
Open Access | Times Cited: 2
A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry
Saurabh Tewari, U. D. Dwivedi, Susham Biswas
Energies (2021) Vol. 14, Iss. 2, pp. 432-432
Open Access | Times Cited: 16
A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field
Ahmad Al-AbdulJabbar, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, et al.
Journal of Sensors (2022) Vol. 2022, pp. 1-14
Open Access | Times Cited: 11
Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process
Chao Gan, Xiang Wang, Lu-Zhao Wang, et al.
Geoenergy Science and Engineering (2023) Vol. 230, pp. 212187-212187
Closed Access | Times Cited: 6
Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method
Oluwatobi Akomolafe, Taoreed O. Owolabi, Mohd Amiruddin Abd Rahman, et al.
Materials (2021) Vol. 14, Iss. 16, pp. 4604-4604
Open Access | Times Cited: 13
Deep Neural Network Prediction of Mechanical Drilling Speed
Haodong Chen, Yan Jin, Wandong Zhang, et al.
Energies (2022) Vol. 15, Iss. 9, pp. 3037-3037
Open Access | Times Cited: 9
A Novel Rate of Penetration Model Based on Support Vector Regression and Modified Bat Algorithm
Yang Zhou, Chengda Lu, Menglin Zhang, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 5, pp. 6659-6668
Closed Access | Times Cited: 9
Analysis of rate of penetration prediction in drilling using data-driven models based on weight on hook measurement
Abbas Hashemizadeh, Ehsan Bahonar, Mohammad Chahardowli, et al.
Earth Science Informatics (2022) Vol. 15, Iss. 4, pp. 2133-2153
Closed Access | Times Cited: 9
Sequential Data Approach for Rate of Penetration Prediction Using Machine Learning Models: A Case Study the Offshore Volve Oil Field, North Sea, Norway
Yanadade Pakawatthapana, Subhorn Khonthapagdee
Lecture notes in networks and systems (2024), pp. 121-130
Closed Access | Times Cited: 1
Research on Adaptive Feature Optimization and Drilling Rate Prediction Based on Real-Time Data
Jun Ren, Jie Jiang, Changchun Zhou, 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
Predicting Rate of Penetration of Horizontal Wells Based on the Di-GRU Model
Tao Pan, Xianzhi Song, Baodong Ma, et al.
Rock Mechanics and Rock Engineering (2024)
Closed Access | Times Cited: 1
Prediction Model of Build-up Rate for Bottom-hole Assembly in Rotary Drilling
Ran Chen, Wenjun Huang, Deli Gao
Geoenergy Science and Engineering (2024), pp. 213610-213610
Closed Access | Times Cited: 1
A hybrid partial least squares regression-based real time pore pressure estimation method for complex geological drilling process
Xi Chen, Weihua Cao, Chao Gan, et al.
Journal of Petroleum Science and Engineering (2021) Vol. 210, pp. 109771-109771
Closed Access | Times Cited: 11
Smart proxy models art and future directions in the oil and gas industry: A review
Eghbal Motaei, Tarek Ganat
Geoenergy Science and Engineering (2023) Vol. 227, pp. 211918-211918
Closed Access | Times Cited: 4
Lost Circulation Prediction Method Based on an Improved Fruit Fly Algorithm for Support Vector Machine Optimization
Song Deng, Chunyu Pei, Xiaopeng Yan, et al.
ACS Omega (2023) Vol. 8, Iss. 36, pp. 32838-32847
Open Access | Times Cited: 4
Intelligent Prediction of Drilling Rate of Penetration Based on Method-Data Dual Validity Analysis
Youwei Wan, Xiangjun Liu, Jian Xiong, et al.
SPE Journal (2023) Vol. 29, Iss. 05, pp. 2257-2274
Closed Access | Times Cited: 4
Physics-Based Rate of the Penetration Prediction Model for Fixed Cutter Drill Bits
Jeronimo de Moura, Jianming Yang, Stephen Butt
Journal of Energy Resources Technology (2020) Vol. 143, Iss. 5
Closed Access | Times Cited: 11
A new spatial modeling method for 3D formation drillability field using fuzzy c-means clustering and random forest
Chao Gan, Weihua Cao, Kang‐Zhi Liu, et al.
Journal of Petroleum Science and Engineering (2021) Vol. 200, pp. 108371-108371
Closed Access | Times Cited: 10
Spatial estimation for 3D formation drillability field: A new modeling framework
Chao Gan, Weihua Cao, Kang‐Zhi Liu, et al.
Journal of Natural Gas Science and Engineering (2020) Vol. 84, pp. 103628-103628
Closed Access | Times Cited: 9

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