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Journal Articles

Comprehensive Evaluation of Microscopic Movability and Macroscopic Productivity of Continental Shale Reservoir

Guoxin Li, Xu Jin, Yinghao Shen, Jiaping Tao, Chang Liu, Songtao Wu, Chenggang Xian, He Liu

Publisher: ASME

J. Energy Resour. Technol.

Paper No: JERT-23-1712

https://doi.org/10.1115/1.4066655

Published Online: September 24, 2024

View Article titled, Comprehensive Evaluation of Microscopic Movability and Macroscopic Productivity of Continental Shale Reservoir

Topics: Reservoirs, Shales, Hydrocarbon reservoirs, Fracture (Materials), Fracture (Process), Compression, Fluid dynamics, Imaging, Nuclear magnetic resonance, Petroleum

Journal Articles

Transforming Oil Well Drilling: Prediction of Real-Time Rate of Penetration with Novel Machine Learning Approach in Varied Lithological Formations

Raunak Gupta, Uttam Kumar Bhui

Publisher: ASME

J. Energy Resour. Technol.

Paper No: JERT-24-1273

https://doi.org/10.1115/1.4066015

Published Online: July 19, 2024

View Article titled, Transforming Oil Well Drilling: Prediction of Real-Time Rate of Penetration with Novel Machine Learning Approach in Varied Lithological Formations

Topics: Drilling, Machine learning, Oil well drilling, Wells, Ablation (Vaporization technology), Algorithms, Artificial neural networks, Bits (Tools), Construction, Errors

Journal Articles

Dynamic Characteristics and Experimental Research of Dual-Piston Axial Oscillation Drag Reduction Tool

Jialin Tian, Lei Tang, Baoyong Yan, Haolin Song, Lanhui Mao

Publisher: ASME

J. Energy Resour. Technol. April 2023, 145(4): 041703.

Paper No: JERT-22-1263

https://doi.org/10.1115/1.4056114

Published Online: November 25, 2022

View Article titled, Dynamic Characteristics and Experimental Research of Dual-Piston Axial Oscillation Drag Reduction Tool

Topics: Displacement, Drag reduction, Drill strings, Oscillations, Pistons, Pressure drop, Drilling, Friction, Fluids, Flow (Dynamics)

Journal Articles

Comparison of Lost Circulation Material Sealing Effectiveness in Water-Based and Oil-Based Drilling Fluids and Under Conditions of Mechanical Shear and High Differential Pressures

Karl Ronny Klungtvedt, Arild Saasen

Publisher: ASME

J. Energy Resour. Technol. December 2022, 144(12): 123011.

Paper No: JERT-22-1218

https://doi.org/10.1115/1.4054653

Published Online: June 10, 2022

View Article titled, Comparison of Lost Circulation Material Sealing Effectiveness in Water-Based and Oil-Based Drilling Fluids and Under Conditions of Mechanical Shear and High Differential Pressures

Topics: Drilling, Fluids, Sealing (Process), Water, Particulate matter, Pressure, Testing, Graphite

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

Publisher: ASME

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

Paper No: JERT-22-1077

https://doi.org/10.1115/1.4054227

Published Online: April 12, 2022

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

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

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 Article titled, 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 Article titled, 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

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

Hans Joakim Skadsem

Publisher: ASME

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

Paper No: JERT-21-1810

https://doi.org/10.1115/1.4053709

Published Online: February 21, 2022

View Article titled, Characterization of Annular Cement Permeability of a Logged Well Section Using Pressure–Pulse Decay Measurements

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

Publisher: ASME

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

Paper No: JERT-21-1683

https://doi.org/10.1115/1.4052772

Published Online: November 9, 2021

View Article titled, Imprinted Glass Fiber-Reinforced Polymer Vascular Networks for Creating Self-Healing Wind Turbine Blades

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

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 Article titled, 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

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 Article titled, 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

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

Erik Andreas Løken, Jens Løkkevik, Dan Sui

Publisher: ASME

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

Paper No: JERT-21-1215

https://doi.org/10.1115/1.4052284

Published Online: September 24, 2021

View Article titled, Testing Machine Learning Algorithms for Drilling Incidents Detection on a Pilot Small-Scale Drilling Rig

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

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 Article titled, 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

Nanomaterials Modified Drilling Fluid for Improving Deep Drilling Conditions

Jamil Abdo, Muhammad Danish Haneef

Publisher: ASME

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

Paper No: JERT-21-1483

https://doi.org/10.1115/1.4052186

Published Online: September 3, 2021

View Article titled, Nanomaterials Modified Drilling Fluid for Improving Deep Drilling Conditions

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

Publisher: ASME

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

Paper No: JERT-20-2181

https://doi.org/10.1115/1.4051896

Published Online: August 11, 2021

View Article titled, Experimental and Numerical Evaluation of Performance of a Variable Pitch Vertical-Axis Wind Turbine

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

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 Article titled, 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 Article titled, 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 Article titled, 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 Article titled, 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

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 Article titled, 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 citation

NARROW

Manuscript

Comprehensive Evaluation of Microscopic Movability and Macroscopic Productivity of Continental Shale Reservoir

Transforming Oil Well Drilling: Prediction of Real-Time Rate of Penetration with Novel Machine Learning Approach in Varied Lithological Formations

Dynamic Characteristics and Experimental Research of Dual-Piston Axial Oscillation Drag Reduction Tool

Comparison of Lost Circulation Material Sealing Effectiveness in Water-Based and Oil-Based Drilling Fluids and Under Conditions of Mechanical Shear and High Differential Pressures

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

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