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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

NOx Emission Predictions in Gas Turbines Through Integrated Data-Driven Machine Learning Approaches

Kazi Ekramul Hoque, Tahiya Hossain, ABM Mominul Haque, Md. Abdul Karim Miah, Md Azazul Haque

Publisher: ASME

J. Energy Resour. Technol. July 2024, 146(7): 071201.

Paper No: JERT-23-1897

https://doi.org/10.1115/1.4065200

Published Online: April 23, 2024

View Article titled, NOx Emission Predictions in Gas Turbines Through Integrated Data-Driven Machine Learning Approaches

Topics: Emissions, Gas turbines, Machine learning, Nitrogen oxides, Algorithms

Journal Articles

An Unsupervised Machine Learning Based Double Sweet Spots Classification and Evaluation Method for Tight Reservoirs

Yuxuan Deng, Wendong Wang, Yuliang Su, Shibo Sun, Xinyu Zhuang

Publisher: ASME

J. Energy Resour. Technol. July 2023, 145(7): 072602.

Paper No: JERT-22-1659

https://doi.org/10.1115/1.4056727

Published Online: February 14, 2023

View Article titled, An Unsupervised Machine Learning Based Double Sweet Spots Classification and Evaluation Method for Tight Reservoirs

Topics: Reservoirs, Algorithms, Machine learning, Weight (Mass), Evaluation methods

Journal Articles

Study of Flow Patterns in a Moving Bed Reactor for Chemical Looping Combustion Based on Machine Learning Methods

Yali Shao, Ramesh K. Agarwal, Xudong Wang, Baosheng Jin

Publisher: ASME

J. Energy Resour. Technol. June 2023, 145(6): 062302.

Paper No: JERT-22-1806

https://doi.org/10.1115/1.4056562

Published Online: January 9, 2023

View Article titled, Study of Flow Patterns in a Moving Bed Reactor for Chemical Looping Combustion Based on Machine Learning Methods

Topics: Artificial neural networks, Feedforward control, Flow (Dynamics), Machine learning, Wedges, Combustion, Discrete element methods

Journal Articles

Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring

Mehrdad Gharib Shirangi, Roger Aragall, Reza Ettehadi Osgouei, Roland May, Edward Furlong, Thomas G. Dahl, Charles A. Thompson

Publisher: ASME

J. Energy Resour. Technol. December 2022, 144(12): 123003.

Paper No: JERT-21-1769

https://doi.org/10.1115/1.4054463

Published Online: May 20, 2022

View Article titled, Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring

Topics: Algorithms, Computational fluid dynamics, Digital twin, Drilling, Fluids, Rheology, Simulation, Machine learning, Rotation

Journal Articles

Investigation on Viscosity Behavior of Anionic Polyacrylamide Copolymer in Brine Solutions for Slickwater Fluids Applications at High Salinity and Hardness Conditions

Dileep Kumar Balaga, Navneeth Kumar Korlepara, Aditya Vyas, Sandeep D. Kulkarni

Publisher: ASME

J. Energy Resour. Technol. November 2022, 144(11): 112104.

Paper No: JERT-21-1590

https://doi.org/10.1115/1.4054226

Published Online: April 26, 2022

View Article titled, Investigation on Viscosity Behavior of Anionic Polyacrylamide Copolymer in Brine Solutions for Slickwater Fluids Applications at High Salinity and Hardness Conditions

Topics: Fluids, Viscosity, Machine learning

Journal Articles

Machine Learning Assisted Analysis of an Ammonia Engine Performance

Zhentao Liu, Jinlong Liu

Publisher: ASME

J. Energy Resour. Technol. November 2022, 144(11): 112307.

Paper No: JERT-21-1965

https://doi.org/10.1115/1.4054287

Published Online: April 26, 2022

View Article titled, Machine Learning Assisted Analysis of an Ammonia Engine Performance

Topics: Artificial neural networks, Combustion, Engines, Fuels, Machine learning, Algorithms, Compression, Ignition

Journal Articles

Digital Twin to Detect Nuclear Proliferation: A Case Study

Christopher Ritter, Ross Hays, Jeren Browning, Ryan Stewart, Samuel Bays, Gustavo Reyes, Mark Schanfein, Adam Pluth, Piyush Sabharwall, Ross Kunz, Ashley Shields, John Koudelka, Porter Zohner

Publisher: ASME

J. Energy Resour. Technol. October 2022, 144(10): 102108.

Paper No: JERT-21-1675

https://doi.org/10.1115/1.4053979

Published Online: March 22, 2022

View Article titled, Digital Twin to Detect Nuclear Proliferation: A Case Study

Topics: Digital twin, Visualization, Machine learning, Manufacturing, Design

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

Localization of Thermal Wellbore Defects Using Machine Learning

Kathryn Bruss, Raymond Kim, Taylor A. Myers, Jiann-Cherng Su, Anirban Mazumdar

Publisher: ASME

J. Energy Resour. Technol. September 2022, 144(9): 093005.

Paper No: JERT-21-1256

https://doi.org/10.1115/1.4053516

Published Online: February 16, 2022

View Article titled, Localization of Thermal Wellbore Defects Using Machine Learning

Topics: Algorithms, Machine learning, Sensors, Simulation, Temperature, Errors, Geothermal engineering, Artificial neural networks

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

Topics: Machine learning, Support vector machines, Water

Journal Articles

Comparison of Random Forest and Neural Network in Modeling the Performance and Emissions of a Natural Gas Spark Ignition Engine

Jinlong Liu, Qiao Huang, Christopher Ulishney, Cosmin E. Dumitrescu

Publisher: ASME

J. Energy Resour. Technol. March 2022, 144(3): 032310.

Paper No: JERT-21-1797

https://doi.org/10.1115/1.4053301

Published Online: January 7, 2022

View Article titled, Comparison of Random Forest and Neural Network in Modeling the Performance and Emissions of a Natural Gas Spark Ignition Engine

Topics: Artificial neural networks, Emissions, Engines, Errors, Machine learning, Natural gas, Algorithms, Combustion, Modeling, Cylinders

Journal Articles

Machine Learning-Based Digital Twins Reduce Seasonal Remapping in Aeroderivative Gas Turbines

Nick Petro, Felipe Lopez

Publisher: ASME

J. Energy Resour. Technol. March 2022, 144(3): 032105.

Paper No: JERT-21-1726

https://doi.org/10.1115/1.4052994

Published Online: December 2, 2021

View Article titled, Machine Learning-Based Digital Twins Reduce Seasonal Remapping in Aeroderivative Gas Turbines

Topics: Acoustics, Combustion, Digital twin, Emissions, Fuels, Gas turbines, Combustion chambers, Temperature, Machine learning, Dynamics (Mechanics)

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 Article titled, 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 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

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

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 citation

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Manuscript

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

NOx Emission Predictions in Gas Turbines Through Integrated Data-Driven Machine Learning Approaches

An Unsupervised Machine Learning Based Double Sweet Spots Classification and Evaluation Method for Tight Reservoirs

Study of Flow Patterns in a Moving Bed Reactor for Chemical Looping Combustion Based on Machine Learning Methods

Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring

Investigation on Viscosity Behavior of Anionic Polyacrylamide Copolymer in Brine Solutions for Slickwater Fluids Applications at High Salinity and Hardness Conditions

Machine Learning Assisted Analysis of an Ammonia Engine Performance

Digital Twin to Detect Nuclear Proliferation: A Case Study

Data-Driven Approach for Resistivity Prediction Using Artificial Intelligence

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

Localization of Thermal Wellbore Defects Using Machine Learning

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

Comparison of Random Forest and Neural Network in Modeling the Performance and Emissions of a Natural Gas Spark Ignition Engine

Machine Learning-Based Digital Twins Reduce Seasonal Remapping in Aeroderivative Gas Turbines

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

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

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

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