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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:

Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids
Shadfar Davoodi, Mohammad Mehrad, David A. Wood, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106459-106459
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning
Shadfar Davoodi, Mohammad Mehrad, David A. Wood, et al.
International Journal of Rock Mechanics and Mining Sciences (2023) Vol. 170, pp. 105546-105546
Closed Access | Times Cited: 28
Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study
Shadfar Davoodi, Hung Vo Thanh, David A. Wood, et al.
Expert Systems with Applications (2024) Vol. 250, pp. 123944-123944
Closed Access | Times Cited: 11
Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids
Shadfar Davoodi, Hung Vo Thanh, David A. Wood, et al.
Journal of Industrial Information Integration (2024) Vol. 41, pp. 100662-100662
Closed Access | Times Cited: 6
Multidimensional Lost Circulation Risk Quantification Assessment Model Based on Ensemble Machine Learning
Haibo Mu, Guancheng Jiang, Wei Zhang, et al.
SPE Journal (2025), pp. 1-11
Closed Access
Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data
Milad Zamanzadeh Talkhouncheh, Shadfar Davoodi, David A. Wood, et al.
Rock Mechanics and Rock Engineering (2024) Vol. 57, Iss. 9, pp. 6881-6907
Closed Access | Times Cited: 4
Advanced Optimized Deep-Learning Model for Precise Evaluation of Subsurface Carbon Dioxide Trapping Efficiency
Shadfar Davoodi, Promise O. Longe, N. V. Makarov, et al.
Energy & Fuels (2025)
Closed Access
Overview of artificial intelligence methods and data analysis techniques suitable for subsurface datasets
David A. Wood
Elsevier eBooks (2025), pp. 1-42
Closed Access
A robust hybrid near-real-time model for prediction of drilling fluids filtration
Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood, et al.
Engineering With Computers (2025)
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
A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time
Mohammed Murif Al-Rubaii, Mohammed Al-Shargabi, Bayan Aldahlawi, et al.
Sensors (2023) Vol. 23, Iss. 14, pp. 6594-6594
Open Access | Times Cited: 10
The Application Potential of Artificial Intelligence and Numerical Simulation in the Research and Formulation Design of Drilling Fluid Gel Performance
Keming Sheng, Yinbo He, Mingliang Du, et al.
Gels (2024) Vol. 10, Iss. 6, pp. 403-403
Open Access | Times Cited: 3
Transition from oil & gas drilling fluids to geothermal drilling fluids
I. R. Collins, Daniel Cano Floriano, Igor Paevskiy, et al.
Geoenergy Science and Engineering (2023) Vol. 233, pp. 212543-212543
Open Access | Times Cited: 7
Hole Cleaning during Drilling Oil and Gas Wells: A Review for Hole-Cleaning Chemistry and Engineering Parameters
Mohammed Murif Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al Shehri
Advances in Materials Science and Engineering (2023) Vol. 2023, pp. 1-33
Open Access | Times Cited: 7
Development of new materials for electrothermal metals using data driven and machine learning
Chengqun Zhou, Muyang Pei, Chao Wu, et al.
PLoS ONE (2024) Vol. 19, Iss. 4, pp. e0297943-e0297943
Open Access | Times Cited: 2
Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey
Haoxin Shi, Yanjun Zhang, Ziwang Yu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Enhancing Fracturing Fluid Viscosity in High Salinity Water: A Data-Driven Approach for Prediction and Optimization
Amro Othman, Zeeshan Tariq, Murtada Saleh Aljawad, et al.
Energy & Fuels (2023) Vol. 37, Iss. 17, pp. 13065-13079
Closed Access | Times Cited: 5
Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis
Irfan Bahiuddin, Saiful Amri Mazlan, Fitrian Imaduddin, et al.
Journal of the Mechanical Behavior of Materials (2024) Vol. 33, Iss. 1
Open Access | Times Cited: 1
Explainable machine-learning-based prediction of equivalent circulating density using surface-based drilling data
Gerald Kelechi Ekechukwu, Abayomi Adejumo
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids
Shadfar Davoodi, Mohammed Murif Al-Rubaii, David A. Wood, et al.
Applied Sciences (2024) Vol. 14, Iss. 19, pp. 9035-9035
Open Access | Times Cited: 1
An integrated intelligent approach to the determination of drilling fluids’ solid content
Shadfar Davoodi, Evgeny Burnaev, David A. Wood, et al.
Colloids and Surfaces A Physicochemical and Engineering Aspects (2024), pp. 135906-135906
Closed Access | Times Cited: 1
A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms
Milad Zamanzadeh Talkhouncheh, Shadfar Davoodi, Babak Larki, et al.
Earth Science Informatics (2023) Vol. 16, Iss. 4, pp. 3387-3416
Closed Access | Times Cited: 3
Prediction of Oil Reservoir Porosity Using Petrophysical Data and a New Intelligent Hybrid Method
Hosnie Nazari, Farnusch Hajizadeh
Pure and Applied Geophysics (2023) Vol. 180, Iss. 12, pp. 4261-4274
Closed Access | Times Cited: 3
Dimensionless Data-Driven Model for Cuttings Concentration Prediction in Eccentric Annuli: Statistical and Parametric Approach
Mohamed Y. Saad, Ahmed A. Gawish, Omar Mahmoud
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 6, pp. 8699-8726
Closed Access
Prediction of Carbon Dioxide Adsorption on Metal-Organic Frameworks: A Combined Machine Learning and Optimization Approach
Promise Longe, Shadfar Davoodi, Mohammad Mehrad
(2024)
Closed Access
Horizontal well flow rate prediction applying machine-learning model
Sergey A. Piskunov, Shadfar Davoodi
Bulletin of the Tomsk Polytechnic University Geo Assets Engineering (2024) Vol. 335, Iss. 5, pp. 118-130
Open Access
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