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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

Statistical Methods to Improve the Quality of Real-Time Drilling Data

Salem Al-Gharbi, Abdulaziz Al-Majed, Abdulazeez Abdulraheem, Zeeshan Tariq, Mohamed Mahmoud

Publisher: ASME

J. Energy Resour. Technol. September 2022, 144(9): 093006.

Paper No: JERT-21-2017

https://doi.org/10.1115/1.4053519

Published Online: March 2, 2022

View Articletitled, Statistical Methods to Improve the Quality of Real-Time Drilling Data

Topics: Algorithms, Drilling, Filters, Filtration, Polynomials, Wavelets

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

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

Generation of Synthetic Sonic Slowness Logs From Real-Time Drilling Sensors Using Artificial Neural Network

Abdulazeez Abdulraheem

Publisher: ASME

J. Energy Resour. Technol. January 2022, 144(1): 013201.

Paper No: JERT-21-1250

https://doi.org/10.1115/1.4052412

Published Online: September 30, 2021

View Articletitled, Generation of Synthetic Sonic Slowness Logs From Real-Time Drilling Sensors Using Artificial Neural Network

Topics: Artificial neural networks, Drilling, Shear (Mechanics), Testing

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

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

A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons

Zeeshan Tariq, Amjed Hassan, Umair Bin Waheed, Mohamed Mahmoud, Dhafer Al-Shehri, Abdulazeez Abdulraheem, Esmail M. A. Mokheimer

Publisher: ASME

J. Energy Resour. Technol. September 2021, 143(9): 092801.

Paper No: JERT-21-1396

https://doi.org/10.1115/1.4051259

Published Online: June 9, 2021

View Articletitled, A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons

Topics: Artificial neural networks, Density, Machine learning, Natural gas, Pressure, Errors, Temperature, Modeling, Molecular weight

Journal Articles

Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques

Osama Mutrif Siddig, Saad Fahaid Al-Afnan, Salaheldin Mahmoud Elkatatny, Abdulazeez Abdulraheem

Publisher: ASME

J. Energy Resour. Technol. February 2022, 144(2): 023001.

Paper No: JERT-21-1326

https://doi.org/10.1115/1.4050960

Published Online: May 13, 2021

View Articletitled, Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques

Topics: Algorithms, Artificial intelligence, Drilling, Elastic moduli, Machine learning, Young's modulus, Rocks

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

Machine Learning-Based Improved Pressure–Volume–Temperature Correlations for Black Oil Reservoirs

Zeeshan Tariq, Mohamed Mahmoud, Abdulazeez Abdulraheem

Publisher: ASME

J. Energy Resour. Technol. November 2021, 143(11): 113003.

Paper No: JERT-21-1031

https://doi.org/10.1115/1.4050579

Published Online: April 19, 2021

View Articletitled, Machine Learning-Based Improved Pressure–Volume–Temperature Correlations for Black Oil Reservoirs

Topics: Algorithms, Artificial neural networks, Hydrocarbon reservoirs, Machine learning, Machinery, Modeling, Particle swarm optimization, Petroleum engineering, Pressure, Reservoirs

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

Productivity Enhancement in Multilayered Unconventional Rocks Using Thermochemicals

Zeeshan Tariq, Mohamed Mahmoud, Olalekan Alade, Abdulazeez Abdulraheem, Ayyaz Mustafa, Esmail M. A. Mokheimer, Murtada Al-Jawad, Ayman Al-Nakhli

Publisher: ASME

J. Energy Resour. Technol. March 2021, 143(3): 033001.

Paper No: JERT-20-1548

https://doi.org/10.1115/1.4047976

Published Online: August 27, 2020

View Articletitled, Productivity Enhancement in Multilayered Unconventional Rocks Using Thermochemicals

Topics: Cements (Adhesives), Fluids, Fracture (Process), Pressure, Rocks, Fracture (Materials), Hydraulic fracturing, Water, Sands, Reservoirs

Journal Articles

An Environment Friendly Approach to Reduce the Breakdown Pressure of High Strength Unconventional Rocks by Cyclic Hydraulic Fracturing

Zeeshan Tariq, Mohamed Mahmoud, Abdulazeez Abdulraheem, Dhafer Al-Shehri, Mobeen Murtaza

Publisher: ASME

J. Energy Resour. Technol. April 2020, 142(4): 043002.

Paper No: JERT-19-1686

https://doi.org/10.1115/1.4045317

Published Online: November 14, 2019

View Articletitled, An Environment Friendly Approach to Reduce the Breakdown Pressure of High Strength Unconventional Rocks by Cyclic Hydraulic Fracturing

Topics: Cements (Adhesives), Fracture (Process), Hydraulic fracturing, Pressure, Rocks, Sands, Fluids, Fracture (Materials)

Journal Articles

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

Tamer Moussa, Salaheldin Elkatatny, Mohamed Mahmoud, Abdulazeez Abdulraheem

Publisher: ASME

J. Energy Resour. Technol. July 2018, 140(7): 072903.

Paper No: JERT-17-1464

https://doi.org/10.1115/1.4039270

Published Online: March 15, 2018

View Articletitled, Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

Topics: Artificial neural networks, Permeability, Reservoirs, Artificial intelligence, Algorithms

Journal Articles

Effect of Sand Content on the Filter Cake Properties and Removal During Drilling Maximum Reservoir Contact Wells in Sandstone Reservoir

Badr S. Ba geri, Mohamed Mahmoud, Saleh. H. Al-Mutairi, Abdulazeez Abdulraheem

Publisher: ASME

J. Energy Resour. Technol. May 2016, 138(3): 032901.

Paper No: JERT-15-1135

https://doi.org/10.1115/1.4032121

Published Online: December 17, 2015

View Articletitled, Effect of Sand Content on the Filter Cake Properties and Removal During Drilling Maximum Reservoir Contact Wells in Sandstone Reservoir

Topics: Drilling, Filters, Fluids, Sands, Porosity, Permeability

Journal Articles

Long-Term Effects of CO 2 Sequestration on Rock Mechanical Properties

Wahbi Abdulqader AL-Ameri, Abdulazeez Abdulraheem, Mohamed Mahmoud

Publisher: ASME

J. Energy Resour. Technol. January 2016, 138(1): 012201.

Paper No: JERT-15-1186

https://doi.org/10.1115/1.4032011

Published Online: December 1, 2015

View Articletitled, Long-Term Effects of CO 2 Sequestration on Rock Mechanical Properties

Topics: Carbon capture and storage, Carbon dioxide, Rocks

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

Statistical Methods to Improve the Quality of Real-Time Drilling Data

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

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

Generation of Synthetic Sonic Slowness Logs From Real-Time Drilling Sensors Using Artificial Neural Network

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

Detection of Loss Zones While Drilling Using Different Machine Learning Techniques

A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons

Drilling Data-Based Approach to Build a Continuous Static Elastic Moduli Profile Utilizing Artificial Intelligence Techniques

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

Machine Learning-Based Improved Pressure–Volume–Temperature Correlations for Black Oil Reservoirs

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

Productivity Enhancement in Multilayered Unconventional Rocks Using Thermochemicals

An Environment Friendly Approach to Reduce the Breakdown Pressure of High Strength Unconventional Rocks by Cyclic Hydraulic Fracturing

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

Effect of Sand Content on the Filter Cake Properties and Removal During Drilling Maximum Reservoir Contact Wells in Sandstone Reservoir

Long-Term Effects of CO 2 Sequestration on Rock Mechanical Properties

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