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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:

Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms
Sina Rashidi, Mohammad Mehrad, Hamzeh Ghorbani, et al.
Journal of Petroleum Science and Engineering (2021) Vol. 202, pp. 108425-108425
Closed Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables
Shadfar Davoodi, Hung Vo Thanh, David A. Wood, et al.
Separation and Purification Technology (2023) Vol. 316, pp. 123807-123807
Closed Access | Times Cited: 45
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Ahmad Chaddad, Qizong Lu, Jiali Li, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 4, pp. 859-876
Open Access | Times Cited: 44
Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms
Meysam Rajabi, Omid Hazbeh, Shadfar Davoodi, et al.
Journal of Petroleum Exploration and Production Technology (2022) Vol. 13, Iss. 1, pp. 19-42
Open Access | Times Cited: 56
Optimized machine learning models for natural fractures prediction using conventional well logs
Somayeh Tabasi, Pezhman Soltani Tehrani, Meysam Rajabi, et al.
Fuel (2022) Vol. 326, pp. 124952-124952
Closed Access | Times Cited: 49
Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms
Mohammad Farsi, Nima Mohamadian, Hamzeh Ghorbani, et al.
Natural Resources Research (2021) Vol. 30, Iss. 5, pp. 3455-3481
Closed Access | Times Cited: 54
Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields
Abouzar Rajabi Behesht Abad, Hamzeh Ghorbani, Nima Mohamadian, et al.
Fuel (2021) Vol. 308, pp. 121872-121872
Open Access | Times Cited: 52
Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well
Omid Hazbeh, Saeed Khezerloo‐ye Aghdam, Hamzeh Ghorbani, et al.
Petroleum Research (2021) Vol. 6, Iss. 3, pp. 271-282
Open Access | Times Cited: 48
Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs
Abouzar Rajabi Behesht Abad, Seyedmohammadvahid Mousavi, Nima Mohamadian, et al.
Journal of Natural Gas Science and Engineering (2021) Vol. 95, pp. 104210-104210
Closed Access | Times Cited: 47
Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data
Meysam Rajabi, Saeed Beheshtian, Shadfar Davoodi, et al.
Journal of Petroleum Exploration and Production Technology (2021) Vol. 11, Iss. 12, pp. 4375-4397
Open Access | Times Cited: 44
Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms
Mohammad Mehrad, Ahmad Ramezanzadeh, Mahdi Bajolvand, et al.
Journal of Petroleum Science and Engineering (2022) Vol. 212, pp. 110254-110254
Closed Access | Times Cited: 34
A Review of Predictive Analytics Models in the Oil and Gas Industries
Putri Azmira R Azmi, Marina Yusoff, Mohamad Taufik Mohd Sallehud-din
Sensors (2024) Vol. 24, Iss. 12, pp. 4013-4013
Open Access | Times Cited: 5
Concatenating data-driven and reduced-physics models for smart production forecasting
Oscar I.O. Ogali, Oyinkepreye D. Orodu
Earth Science Informatics (2025) Vol. 18, Iss. 2
Closed Access
A Comparative Study of Ensemble Learning Techniques and Mathematical Models for Rigorous Modeling of Solution Gas/Oil Ratio
Hossein Yavari, Jafar Qajar
SPE Journal (2025), pp. 1-26
Closed Access
Predicting oil flow rate through orifice plate with robust machine learning algorithms
Abouzar Rajabi Behesht Abad, Pezhman Soltani Tehrani, Mohammad Naveshki, et al.
Flow Measurement and Instrumentation (2021) Vol. 81, pp. 102047-102047
Closed Access | Times Cited: 39
Data driven models to predict pore pressure using drilling and petrophysical data
Farshad Jafarizadeh, Meysam Rajabi, Somayeh Tabasi, et al.
Energy Reports (2022) Vol. 8, pp. 6551-6562
Open Access | Times Cited: 26
New insights into permeability determination by coupling Stoneley wave propagation and conventional petrophysical logs in carbonate oil reservoirs
Alireza Rostami, Ali Kordavani, Shahin Parchekhari, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 23
Knowledge-based machine learning for mineral classification in a complex tectonic regime of Yingxiu-Beichuan fault zone, Sichuan basin
Jar Ullah, Huan Li, Umar Ashraf, et al.
Geoenergy Science and Engineering (2023) Vol. 229, pp. 212077-212077
Closed Access | Times Cited: 15
Novel robust Elman neural network-based predictive models for bubble point oil formation volume factor and solution gas–oil ratio using experimental data
Kamyab Kohzadvand, Maryam Mahmoudi Kouhi, Mehdi Ghasemi, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 23, pp. 14503-14526
Closed Access | Times Cited: 4
Robust computational approach to determine the safe mud weight window using well-log data from a large gas reservoir
Saeed Beheshtian, Meysam Rajabi, Shadfar Davoodi, et al.
Marine and Petroleum Geology (2022) Vol. 142, pp. 105772-105772
Closed Access | Times Cited: 19
Prediction of fracture density in a gas reservoir using robust computational approaches
Guozhong Gao, Omid Hazbeh, Shadfar Davoodi, et al.
Frontiers in Earth Science (2023) Vol. 10
Open Access | Times Cited: 10
A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield
Farshad Jafarizadeh, Babak Larki, Bamdad Kazemi, et al.
Petroleum (2022) Vol. 9, Iss. 3, pp. 468-485
Open Access | Times Cited: 18
Petroleum Well Blowouts as a Threat to Drilling Operation and Wellbore Sustainability: Causes, Prevention, Safety and Emergency Response
Mohammad Reza Abdali, Nima Mohamadian, Hamzeh Ghorbani, et al.
Journal of Construction Materials (2021)
Open Access | Times Cited: 21
Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods
Shamil Islamov, Alexey Grigoriev, И. И. Белоглазов, et al.
Symmetry (2021) Vol. 13, Iss. 7, pp. 1293-1293
Open Access | Times Cited: 21
Research on prediction methods of formation pore pressure based on machine learning
Honglin Huang, Jun Li, Hongwei Yang, et al.
Energy Science & Engineering (2022) Vol. 10, Iss. 6, pp. 1886-1901
Open Access | Times Cited: 15
Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm
Omid Hazbeh, Mehdi Ahmadi Alvar, Saeed Khezerloo‐ye Aghdam, et al.
Journal of Petroleum and Mining Engineering (2021), pp. 14-27
Open Access | Times Cited: 19
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