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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:

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

Showing 1-25 of 44 citing articles:

Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis
Nitish Gupta, Yashvi Mohta, Khyati Heda, et al.
Journal of Environmental and Public Health (2023) Vol. 2023, pp. 1-26
Open Access | Times Cited: 58
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: 54
A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques
Guodao Zhang, Shadfar Davoodi, Shahab S. Band, et al.
Energy Reports (2022) Vol. 8, pp. 2233-2247
Open Access | Times Cited: 38
Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption
Leren Qian, Zhongsheng Chen, Yiqian Huang, et al.
Urban Climate (2023) Vol. 51, pp. 101647-101647
Closed Access | Times Cited: 37
Seismic driven reservoir classification using advanced machine learning algorithms: A case study from the Lower Ranikot/Khadro sandstone gas reservoir, Kirthar Fold Belt, Lower Indus Basin, Pakistan
Umar Manzoor, Muhsan Ehsan, Ahmed E. Radwan, et al.
Geoenergy Science and Engineering (2023) Vol. 222, pp. 211451-211451
Closed Access | Times Cited: 32
A hybrid machine learning approach based study of production forecasting and factors influencing the multiphase flow through surface chokes
Waquar Kaleem, Saurabh Tewari, Mrigya Fogat, et al.
Petroleum (2023) Vol. 10, Iss. 2, pp. 354-371
Open Access | Times Cited: 19
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
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: 25
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
Robust fracture intensity estimation from petrophysical logs and mud loss data: a multi-level ensemble modeling approach
Ahmad Azadivash, Hosseinali Soleymani, Atrina Seifirad, et al.
Journal of Petroleum Exploration and Production Technology (2024) Vol. 14, Iss. 7, pp. 1859-1878
Open Access | Times Cited: 4
Predicting formation fracture characteristics derived from borehole image data with petrophysical well-log variables
David A. Wood
Elsevier eBooks (2025), pp. 287-328
Closed Access
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
A novel ensemble machine learning model to predict mine blasting–induced rock fragmentation
Mojtaba Yari, Biao He, Danial Jahed Armaghani, et al.
Bulletin of Engineering Geology and the Environment (2023) Vol. 82, Iss. 5
Closed Access | Times Cited: 12
Applied machine learning-based models for predicting the geomechanical parameters using logging data
Manouchehr Sanei, Ahmad Ramezanzadeh, Mohammad Reza Delavar
Journal of Petroleum Exploration and Production Technology (2023) Vol. 13, Iss. 12, pp. 2363-2385
Open Access | Times Cited: 12
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
Reservoir rock typing for optimum permeability prediction of Nubia formation in October Field, Gulf of Suez, Egypt
Mohamed A. Kassab, Ali Abbas, Ihab Abdel Latif Osman, et al.
Journal of Petroleum Exploration and Production Technology (2024) Vol. 14, Iss. 6, pp. 1395-1416
Open Access | Times Cited: 3
Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks
Muhammad Luqman Hasan, Tivadar M. Tóth
Journal of Petroleum Exploration and Production Technology (2024) Vol. 14, Iss. 7, pp. 1899-1921
Open Access | Times Cited: 3
Optimization of input parameters of ANN–driven plasma source through nature-inspired evolutionary algorithms
Vipin Shukla, M. Bandyopadhyay
Intelligent Systems with Applications (2023) Vol. 18, pp. 200200-200200
Open Access | Times Cited: 8
Identification of natural fractures in shale gas reservoirs using fracture signature function and machine learning models
Atif Ismail, Farshid Torabi, Saman Azadbakht, et al.
Unconventional Resources (2023) Vol. 4, pp. 100069-100069
Open Access | Times Cited: 8
Predicting stress-dependent gas permeability of cement mortar with different relative moisture contents based on hybrid ensemble artificial intelligence algorithms
Zhiming Chao, Mingyang Wang, Yinuo Sun, et al.
Construction and Building Materials (2022) Vol. 348, pp. 128660-128660
Closed Access | Times Cited: 13
Implications of machine learning on geomechanical characterization and sand management: a case study from Hilal field, Gulf of Suez, Egypt
Wael K. Abdelghany, Mohamed Hammed, Ahmed E. Radwan, et al.
Journal of Petroleum Exploration and Production Technology (2022) Vol. 13, Iss. 1, pp. 297-312
Open Access | Times Cited: 12
Evolutionary Computation in Artificial Intelligence
Dharmesh Dhabliya, Ankur Gupta, Sukhvinder Singh Dari, et al.
Advances in computational intelligence and robotics book series (2024), pp. 176-194
Closed Access | Times Cited: 2
Applied machine learning-based models for determining the magnitude of pore pressure and minimum horizontal stress
Manouchehr Sanei, Ahmad Ramezanzadeh, Amin Asgari
Arabian Journal of Geosciences (2024) Vol. 17, Iss. 7
Open Access | Times Cited: 2
Optimizing the evaluation model of green building management based on the concept of urban ecology and environment
Chengxi Lyu, Jiaxi Hu, Rui Zhang, et al.
Frontiers in Ecology and Evolution (2023) Vol. 10
Open Access | Times Cited: 6
Application of GMDH model to predict pore pressure
Guozhong Gao, Omid Hazbeh, Meysam Rajabi, et al.
Frontiers in Earth Science (2023) Vol. 10
Open Access | Times Cited: 5
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