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Journal Articles

Modeling and experimental evaluation of the effects of reflective film and vacuum on the performance of concentric double tube direct flow solar collector

K.A.R. Ismail, Mavd de Paula Ribeiro Teles, Fatima A. M. Lino

J. Energy Resour. Technol.

Paper No: JERT-22-1063

DOI: https://doi.org/10.1115/1.4054532

Published Online: May 11, 2022

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Topics: Flow (Dynamics), Modeling, Solar collectors, Vacuum, Renewable energy, Solar films, Energy generation, Errors, Heat, Matlab

Journal Articles

Employing Deep Learning Neural Networks for Characterizing Dual-Porosity Reservoirs Based on Pressure Transient Tests

Rakesh Kumar Pandey, Anil Kumar, Ajay Mandal, Behzad Vaferi

J. Energy Resour. Technol. November 2022, 144(11): 113002.

Paper No: JERT-22-1077

DOI: https://doi.org/10.1115/1.4054227

Published Online: April 12, 2022

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Topics: Artificial neural networks, Porosity, Pressure, Reservoirs, Transients (Dynamics), Regression models, Signals, Testing, Noise (Sound)

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

Characterization of Annular Cement Permeability of a Logged Well Section Using Pressure–Pulse Decay Measurements

Hans Joakim Skadsem

J. Energy Resour. Technol. May 2022, 144(5): 053004.

Paper No: JERT-21-1810

DOI: https://doi.org/10.1115/1.4053709

Published Online: February 21, 2022

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Topics: Cements (Adhesives), Permeability, Pressure, Pressure measurement, Reservoirs, Annulus, Testing

Journal Articles

Imprinted Glass Fiber-Reinforced Polymer Vascular Networks for Creating Self-Healing Wind Turbine Blades

Ryoichi S. Amano, Giovanni Lewinski, Rulin Shen

J. Energy Resour. Technol. June 2022, 144(6): 062107.

Paper No: JERT-21-1683

DOI: https://doi.org/10.1115/1.4052772

Published Online: November 9, 2021

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Topics: Additive manufacturing, Blades, Catalysts, Epoxy adhesives, Epoxy resins, Fiber reinforced plastics, Fibers, Glass, Polymers, Testing

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

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

Abdulazeez Abdulraheem

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

Paper No: JERT-21-1250

DOI: https://doi.org/10.1115/1.4052412

Published Online: September 30, 2021

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

Journal Articles

Testing Machine Learning Algorithms for Drilling Incidents Detection on a Pilot Small-Scale Drilling Rig

Erik Andreas Løken, Jens Løkkevik, Dan Sui

J. Energy Resour. Technol. December 2021, 143(12): 124501.

Paper No: JERT-21-1215

DOI: https://doi.org/10.1115/1.4052284

Published Online: September 24, 2021

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Topics: Drilling, Machine learning, Drilling rigs, Testing, Algorithms, Pipes, Vibration

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

Nanomaterials Modified Drilling Fluid for Improving Deep Drilling Conditions

Jamil Abdo, Muhammad Danish Haneef

J. Energy Resour. Technol. July 2022, 144(7): 073202.

Paper No: JERT-21-1483

DOI: https://doi.org/10.1115/1.4052186

Published Online: September 3, 2021

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Topics: Drilling, Fluids, Nanoclays, Rheology, Temperature, Testing, Pressure, Viscosity, Yield point, Nanomaterials

Journal Articles

Experimental and Numerical Evaluation of Performance of a Variable Pitch Vertical-Axis Wind Turbine

Owaisur Rahman Shah, Muhammad Azfar Jamal, Talha Irfan Khan, Usama Waleed Qazi

J. Energy Resour. Technol. June 2022, 144(6): 061303.

Paper No: JERT-20-2181

DOI: https://doi.org/10.1115/1.4051896

Published Online: August 11, 2021

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Topics: Blades, Design, Drag (Fluid dynamics), Rotors, Torque, Vertical axis wind turbines, Wind turbines, Airfoils, Flow (Dynamics), Wind tunnels

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

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

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

Zeeshan Tariq, Mohamed Mahmoud, Abdulazeez Abdulraheem

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

Paper No: JERT-21-1031

DOI: https://doi.org/10.1115/1.4050579

Published Online: April 19, 2021

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

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Journal citation

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Manuscript

Modeling and experimental evaluation of the effects of reflective film and vacuum on the performance of concentric double tube direct flow solar collector

Employing Deep Learning Neural Networks for Characterizing Dual-Porosity Reservoirs Based on Pressure Transient Tests

Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence

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

Characterization of Annular Cement Permeability of a Logged Well Section Using Pressure–Pulse Decay Measurements

Imprinted Glass Fiber-Reinforced Polymer Vascular Networks for Creating Self-Healing Wind Turbine Blades

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

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

Testing Machine Learning Algorithms for Drilling Incidents Detection on a Pilot Small-Scale Drilling Rig

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

Nanomaterials Modified Drilling Fluid for Improving Deep Drilling Conditions

Experimental and Numerical Evaluation of Performance of a Variable Pitch Vertical-Axis Wind Turbine

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

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

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

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