Mohammed Al-Shargabi

Author

National Research Tomsk State University ORCID

Also Known As

Al-Shargabi Mohammed, Al-Shargabi, Mohammed, Alhasan Mohammed, M. A. Al-Shargabi, Mohammed A. Al-Shargabi, Mohammed Al-Shargabi, Mohammed Al-Shargabid, Mohammed. A. Al-Shargabi, Mohammed. A. Al–Shargabi

Publications

1.6K

Citations

H-Index

Concepts

All Affiliations

National Research Tomsk State University Tomsk Polytechnic University King Fahd University of Petroleum and Minerals EDF Energy (United Kingdom)

About

Mohammed Al-Shargabi is an author at National Research Tomsk State University specializing in engineering, industrial engineering, and formation damage.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	6	6	397
2	Chemical Engineering	2	2	322
3	Civil Engineering	1	1	16
4	Computer Engineering	1	1	19
5	Computer Science	1	1	19

Mohammed Al-Shargabi

Publications

440

Citations

H-Index

Page 1

	Year	Citations
Review of technological progress in carbon dioxide capture, storage, and utilization Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood, Gas Science and Engineering Chemical EngineeringCarbon SequestrationEngineeringCarbonizationCarbon Utilization	2023	250
Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Separation and Purification Technology Machine-learning ModelsChemical EngineeringPore StructureEngineeringMachine Learning	2023	72
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, Expert Systems with Applications Carbon SequestrationFive-spot PatternTheoretical StudyData ScienceEngineering	2024	25
Decision tree rule learning approach to counter burst header packet flooding attack in Optical Burst Switching network Adel Rajab, Chin‐Tser Huang, Mohammed Al-Shargabi Optical Switching and Networking EngineeringComputer EngineeringComputer ScienceDecision Tree RuleOptical Networking	2018	19
A Novel Efficient Borehole Cleaning Model for Optimizing Drilling Performance in Real Time Mohammed Murif Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al Shehri, Applied Sciences Geotechnical EngineeringEngineeringDrilling FluidsIndustrial EngineeringDrilling Industry	2023	16
Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Geoenergy Science and Engineering	2024	16
A Novel Automated Model for Evaluation of the Efficiency of Hole Cleaning Conditions during Drilling Operations Mohammed Murif Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al Shehri Applied Sciences Hole Cleaning ConditionsEngineeringDrilling FluidsIndustrial EngineeringMechanical Engineering	2023	15
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, Sensors	2023	14
A Novel Model for the Real-Time Evaluation of Hole-Cleaning Conditions with Case Studies Mohammed Murif Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al Shehri Energies	2023	13

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