Page 1 | Search Results | ASME Digital Collection

1-20 of 31

Sort by

Sort Order Select

Journal Articles

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

Badr Bageri, Jaber Al Jaberi, Salaheldin Elkatatny

Publisher: ASME

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

Paper No: JERT-22-1460

https://doi.org/10.1115/1.4055209

Published Online: September 1, 2022

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

Topics: Drilling, Filters, Filtration, Fluids, Water

Journal Articles

Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence

Ahmed Abdelaal, Ahmed Farid Ibrahim, Salaheldin Elkatatny

Publisher: ASME

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

Paper No: JERT-22-1087

https://doi.org/10.1115/1.4053954

Published Online: March 18, 2022

View Articletitled, Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence

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

Publisher: ASME

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

Paper No: JERT-21-2104

https://doi.org/10.1115/1.4053846

Published Online: March 3, 2022

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

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

Publisher: ASME

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

Paper No: JERT-21-2015

https://doi.org/10.1115/1.4053439

Published Online: February 9, 2022

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

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

Publisher: ASME

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

Paper No: JERT-21-1961

https://doi.org/10.1115/1.4053248

Published Online: January 18, 2022

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

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

Publisher: ASME

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

Paper No: JERT-21-1799

https://doi.org/10.1115/1.4052794

Published Online: November 12, 2021

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

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

Publisher: ASME

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

Paper No: JERT-21-1741

https://doi.org/10.1115/1.4052485

Published Online: October 5, 2021

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

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

Publisher: ASME

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

Paper No: JERT-21-1559

https://doi.org/10.1115/1.4052181

Published Online: September 3, 2021

View Articletitled, Sagging Prevention for Hematite-Based Invert Emulsion Mud

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

Publisher: ASME

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

Paper No: JERT-21-1643

https://doi.org/10.1115/1.4052185

Published Online: September 3, 2021

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

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

Publisher: ASME

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

Paper No: JERT-21-1539

https://doi.org/10.1115/1.4051737

Published Online: July 19, 2021

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

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

Publisher: ASME

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

Paper No: JERT-21-1527

https://doi.org/10.1115/1.4051670

Published Online: July 16, 2021

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

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

Publisher: ASME

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

Paper No: JERT-21-1499

https://doi.org/10.1115/1.4051553

Published Online: July 15, 2021

View Articletitled, Detection of Loss Zones While Drilling Using Different Machine Learning Techniques

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

Publisher: ASME

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

Paper No: JERT-21-1424

https://doi.org/10.1115/1.4051298

Published Online: June 14, 2021

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

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

Publisher: ASME

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

Paper No: JERT-20-1880

https://doi.org/10.1115/1.4051197

Published Online: May 31, 2021

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

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

Publisher: ASME

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

Paper No: JERT-20-1898

https://doi.org/10.1115/1.4050843

Published Online: May 3, 2021

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

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

Publisher: ASME

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

Paper No: JERT-20-1977

https://doi.org/10.1115/1.4050777

Published Online: April 29, 2021

View Articletitled, 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

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

Publisher: ASME

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

Paper No: JERT-20-2001

https://doi.org/10.1115/1.4050778

Published Online: April 29, 2021

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

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

Publisher: ASME

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

Paper No: JERT-20-1633

https://doi.org/10.1115/1.4049125

Published Online: December 8, 2020

View Articletitled, Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

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

Publisher: ASME

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

Paper No: JERT-20-1090

https://doi.org/10.1115/1.4048070

Published Online: August 28, 2020

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

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

Publisher: ASME

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

Paper No: JERT-19-1124

https://doi.org/10.1115/1.4045169

Published Online: October 23, 2019

View Articletitled, New Hybrid Hole Cleaning Model for Vertical and Deviated Wells

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

Update search

Journal citation

NARROW

Format

Publisher

Journal

Article Type

Subject Area

Topics

Date

Availability

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

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

Update search

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

This Feature Is Available To Subscribers Only