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Journal Articles

Effect of Hematite Dosage on Water-Based Drilling Fluid and Filter Cake Properties

Badr Bageri, Jaber Al Jaberi, Salaheldin Elkatatny

J. Energy Resour. Technol. March 2023, 145(3): 031601.

Paper No: JERT-22-1460

DOI: https://doi.org/10.1115/1.4055209

Published Online: September 1, 2022

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Topics: Drilling, Filters, Filtration, Fluids, Water

Journal Articles

Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence

Ahmed Abdelaal, Ahmed Farid Ibrahim, Salaheldin Elkatatny

J. Energy Resour. Technol. October 2022, 144(10): 103003.

Paper No: JERT-22-1087

DOI: https://doi.org/10.1115/1.4053954

Published Online: March 18, 2022

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Topics: Electrical resistivity, Support vector machines, Machine learning, Reservoirs, Wells, Testing

Journal Articles

Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations

Vagif Suleymanov, Hany Gamal, Salaheldin Elkatatny, Guenther Glatz, Abdulazeez Abdulraheem

J. Energy Resour. Technol. October 2022, 144(10): 103201.

Paper No: JERT-21-2104

DOI: https://doi.org/10.1115/1.4053846

Published Online: March 3, 2022

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Topics: Acoustics, Drilling, Machine learning, Sensitivity analysis, Support vector machines, Testing, Composite materials, Shear (Mechanics), Wells

Journal Articles

Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality

Salem Al-Gharbi, Abdulaziz Al-Majed, Salaheldin Elkatatny, Abdulazeez Abdulraheem

J. Energy Resour. Technol. September 2022, 144(9): 093002.

Paper No: JERT-21-2015

DOI: https://doi.org/10.1115/1.4053439

Published Online: February 9, 2022

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Topics: Artificial neural networks, Drilling, Machine learning, Support vector machines

Journal Articles

Application of Various Machine Learning Techniques in Predicting Water Saturation in Tight Gas Sandstone Formation

Ahmed Farid Ibrahim, Salaheldin Elkatatny, Yasmin Abdelraouf, Mustafa Al Ramadan

J. Energy Resour. Technol. August 2022, 144(8): 083009.

Paper No: JERT-21-1961

DOI: https://doi.org/10.1115/1.4053248

Published Online: January 18, 2022

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Topics: Machine learning, Support vector machines, Water

Journal Articles

Intelligent Model for Predicting Downhole Vibrations Using Surface Drilling Data During Horizontal Drilling

Ramy Saadeldin, Hany Gamal, Salaheldin Elkatatny, Abdulazeez Abdulraheem

J. Energy Resour. Technol. August 2022, 144(8): 083002.

Paper No: JERT-21-1799

DOI: https://doi.org/10.1115/1.4052794

Published Online: November 12, 2021

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Topics: Artificial neural networks, Drilling, Vibration, Machine learning

Journal Articles

Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs

Ahmed Farid Ibrahim, Redha Al-Dhaif, Salaheldin Elkatatny, Dhafer Al Shehri

J. Energy Resour. Technol. January 2022, 144(1): 013003.

Paper No: JERT-21-1741

DOI: https://doi.org/10.1115/1.4052485

Published Online: October 5, 2021

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Topics: Flow (Dynamics), Machine learning, Reservoirs, Support vector machines, Testing

Journal Articles

Sagging Prevention for Hematite-Based Invert Emulsion Mud

Ashraf Ahmed, Salem Basfar, Salaheldin Elkatatny

J. Energy Resour. Technol. July 2022, 144(7): 073003.

Paper No: JERT-21-1559

DOI: https://doi.org/10.1115/1.4052181

Published Online: September 3, 2021

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Topics: Density, Drilling, Emulsions, Filtration, Fluids, Rheology, Stability, High temperature, Particulate matter, Filters

Journal Articles

Applying Different Artificial Intelligence Techniques in Dynamic Poisson’s Ratio Prediction Using Drilling Parameters

Osama Siddig, Hany Gamal, Salaheldin Elkatatny, Abdulazeez Abdulraheem

J. Energy Resour. Technol. July 2022, 144(7): 073006.

Paper No: JERT-21-1643

DOI: https://doi.org/10.1115/1.4052185

Published Online: September 3, 2021

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Topics: Drilling, Machine learning, Poisson ratio, Support vector machines, Algorithms, Testing, Wells, Rocks

Journal Articles

Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach

Ahmed Abdulhamid Mahmoud, Hany Gamal, Salaheldin Elkatatny, Ahmed Alsaihati

J. Energy Resour. Technol. April 2022, 144(4): 043004.

Paper No: JERT-21-1539

DOI: https://doi.org/10.1115/1.4051737

Published Online: July 19, 2021

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Topics: Algorithms, Carbon, Machine learning, Testing, Gamma rays, Fuzzy neural nets, Potassium, Uranium, Drilling

Journal Articles

Predicting the Rock Sonic Logs While Drilling by Random Forest and Decision Tree-Based Algorithms

Hany Gamal, Ahmed Alsaihati, Salaheldin Elkatatny

J. Energy Resour. Technol. April 2022, 144(4): 043203.

Paper No: JERT-21-1527

DOI: https://doi.org/10.1115/1.4051670

Published Online: July 16, 2021

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Topics: Algorithms, Drilling, Machine learning, Rocks, Testing

Journal Articles

Detection of Loss Zones While Drilling Using Different Machine Learning Techniques

Ahmed Alsaihati, Mahmoud Abughaban, Salaheldin Elkatatny, Abdulazeez Abdulraheem

J. Energy Resour. Technol. April 2022, 144(4): 043202.

Paper No: JERT-21-1499

DOI: https://doi.org/10.1115/1.4051553

Published Online: July 15, 2021

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Topics: Drilling, Machine learning, Support vector machines, Testing, Wells, Fluids

Journal Articles

Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques

Redha Al Dhaif, Ahmed Farid Ibrahim, Salaheldin Elkatatny

J. Energy Resour. Technol. March 2022, 144(3): 033001.

Paper No: JERT-21-1424

DOI: https://doi.org/10.1115/1.4051298

Published Online: June 14, 2021

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Topics: Artificial intelligence, Flow (Dynamics), Reservoirs, Wells, Pressure, Testing, Condensed matter

Journal Articles

The Utilization of Steelmaking Industrial Waste of Silicomanganese Fume as Filtration Loss Control in Drilling Fluid Application

Waseem Razzaq, Salaheldin Elkatatny, Hany Gamal, Ariffin Samsuri

J. Energy Resour. Technol. February 2022, 144(2): 023004.

Paper No: JERT-20-1880

DOI: https://doi.org/10.1115/1.4051197

Published Online: May 31, 2021

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Topics: Drilling, Filtration, Fluids, Silicomanganese, Iron and steel making, Water

Journal Articles

Rock Strength Prediction in Real-Time While Drilling Employing Random Forest and Functional Network Techniques

Hany Gamal, Ahmed Alsaihati, Salaheldin Elkatatny, Saleh Haidary, Abdulazeez Abdulraheem

J. Energy Resour. Technol. September 2021, 143(9): 093004.

Paper No: JERT-20-1898

DOI: https://doi.org/10.1115/1.4050843

Published Online: May 3, 2021

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Topics: Artificial intelligence, Drilling, Principal component analysis, Rocks, Testing, Errors, Reservoirs, Compressive strength, Petroleum industry

Journal Articles

Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale From the Spectral Gamma-Ray and Based on the Artificial Neural Networks

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny

J. Energy Resour. Technol. September 2021, 143(9): 093002.

Paper No: JERT-20-1977

DOI: https://doi.org/10.1115/1.4050777

Published Online: April 29, 2021

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Topics: Artificial neural networks, Carbon, Shales, Testing, Gamma rays, Potassium, Uranium

Journal Articles

Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest

Hany Osman, Abdulwahab Ali, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny

J. Energy Resour. Technol. September 2021, 143(9): 093003.

Paper No: JERT-20-2001

DOI: https://doi.org/10.1115/1.4050778

Published Online: April 29, 2021

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Topics: Artificial neural networks, Drilling, Machine learning, Regression models, Algorithms, Testing

Journal Articles

Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

Abdulmalek Ahmed, Salaheldin Elkatatny, Abdulwahab Ali

J. Energy Resour. Technol. March 2021, 143(3): 033201.

Paper No: JERT-20-1633

DOI: https://doi.org/10.1115/1.4049125

Published Online: December 8, 2020

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Topics: Artificial neural networks, Fracture (Materials), Fracture (Process), Pressure, Testing, Artificial intelligence, Support vector machines, Drilling

Journal Articles

Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities While Drilling Horizontal Wells, With Real Case Study

Ahmed Alsaihati, Salaheldin Elkatatny, Ahmed Abdulhamid Mahmoud, Abdulazeez Abdulraheem

J. Energy Resour. Technol. April 2021, 143(4): 043201.

Paper No: JERT-20-1090

DOI: https://doi.org/10.1115/1.4048070

Published Online: August 28, 2020

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Topics: Artificial neural networks, Drilling, Torque, Wells, Testing, Drag (Fluid dynamics), Pipes

Journal Articles

New Hybrid Hole Cleaning Model for Vertical and Deviated Wells

Ahmed Abdulhamid Mahmoud, Mahmoud Elzenary, Salaheldin Elkatatny

J. Energy Resour. Technol. March 2020, 142(3): 034501.

Paper No: JERT-19-1124

DOI: https://doi.org/10.1115/1.4045169

Published Online: October 23, 2019

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Topics: Cutting, Drilling, Fluids, High cycle fatigue, Wells, Drills (Tools), Pipes, Rheology, Computer software, Density

Journal citation

NARROW

Effect of Hematite Dosage on Water-Based Drilling Fluid and Filter Cake Properties

Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence

Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations

Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data Quality

Application of Various Machine Learning Techniques in Predicting Water Saturation in Tight Gas Sandstone Formation

Intelligent Model for Predicting Downhole Vibrations Using Surface Drilling Data During Horizontal Drilling

Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs

Sagging Prevention for Hematite-Based Invert Emulsion Mud

Applying Different Artificial Intelligence Techniques in Dynamic Poisson’s Ratio Prediction Using Drilling Parameters

Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach

Predicting the Rock Sonic Logs While Drilling by Random Forest and Decision Tree-Based Algorithms

Detection of Loss Zones While Drilling Using Different Machine Learning Techniques

Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques

The Utilization of Steelmaking Industrial Waste of Silicomanganese Fume as Filtration Loss Control in Drilling Fluid Application

Rock Strength Prediction in Real-Time While Drilling Employing Random Forest and Functional Network Techniques

Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale From the Spectral Gamma-Ray and Based on the Artificial Neural Networks

Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest

Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

Use of Machine Learning and Data Analytics to Detect Downhole Abnormalities While Drilling Horizontal Wells, With Real Case Study

New Hybrid Hole Cleaning Model for Vertical and Deviated Wells

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