Page 1 | Search Results | J. Energy Resour. Technol.

1-16 of 16

Sort by

Sort Order Select

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

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

A Comparative Analysis of Exhaust Gas Temperature Based on Machine Learning Models for Aviation Applications

Vehbi Emrah Atasoy, Ahmet Esat Suzer, Selcuk Ekici

Publisher: ASME

J. Energy Resour. Technol. August 2022, 144(8): 082101.

Paper No: JERT-21-1512

https://doi.org/10.1115/1.4052771

Published Online: November 12, 2021

View Article titled, A Comparative Analysis of Exhaust Gas Temperature Based on Machine Learning Models for Aviation Applications

Topics: Exhaust systems, Flight, Support vector machines, Temperature, Aircraft engines, Engines

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

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

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

Predictive Modeling for Rooftop Solar Energy Throughput: A Machine Learning-Based Optimization for Building Energy Demand Scheduling

Mahdi Houchati, AbdlMonem H. Beitelmal, Marwan Khraisheh

Publisher: ASME

J. Energy Resour. Technol. January 2022, 144(1): 011302.

Paper No: JERT-21-1073

https://doi.org/10.1115/1.4050844

Published Online: May 7, 2021

View Article titled, Predictive Modeling for Rooftop Solar Energy Throughput: A Machine Learning-Based Optimization for Building Energy Demand Scheduling

Topics: Algorithms, Energy consumption, Optimization, Solar energy, Stress, Support vector machines, Temperature, Intelligent buildings, Machinery, Modeling

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 Article titled, 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

Committee Machine-Ensemble as a General Paradigm for Accurate Prediction of Bubble Point Pressure of Crude Oil

Sina Rashidi, Mohammad Khajehesfandeari

Publisher: ASME

J. Energy Resour. Technol. February 2021, 143(2): 023006.

Paper No: JERT-20-1575

https://doi.org/10.1115/1.4047977

Published Online: August 24, 2020

View Article titled, Committee Machine-Ensemble as a General Paradigm for Accurate Prediction of Bubble Point Pressure of Crude Oil

Topics: Artificial neural networks, Bubbles, Crude oil, Machinery, Pressure, Support vector machines, Reservoirs

Journal Articles

Estimation of CO 2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

Qihong Feng, Ronghao Cui, Sen Wang, Jin Zhang, Zhe Jiang

Publisher: ASME

J. Energy Resour. Technol. April 2019, 141(4): 041001.

Paper No: JERT-18-1524

https://doi.org/10.1115/1.4041724

Published Online: November 19, 2018

View Article titled, Estimation of CO 2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

Topics: Artificial neural networks, Carbon dioxide, Diffusion (Physics), Genetic algorithms, Support vector machines, Temperature, Viscosity

Includes: Supplementary data

Journal Articles

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

Nan Wei, Changjun Li, Chan Li, Hanyu Xie, Zhongwei Du, Qiushi Zhang, Fanhua Zeng

Publisher: ASME

J. Energy Resour. Technol. March 2019, 141(3): 032701.

Paper No: JERT-18-1338

https://doi.org/10.1115/1.4041413

Published Online: October 1, 2018

View Article titled, Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

Topics: Natural gas, Selection algorithm, Support vector machines, Genetic algorithms, Errors, Cycles

Journal Articles

Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

Salaheldin Elkatatny

Publisher: ASME

J. Energy Resour. Technol. July 2018, 140(7): 072905.

Paper No: JERT-17-1497

https://doi.org/10.1115/1.4039613

Published Online: March 29, 2018

View Article titled, Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

Topics: Artificial intelligence, Artificial neural networks, Poisson ratio, Reservoirs, Drilling, Support vector machines, Density

Journal Articles

Support Vector Regression and Computational Fluid Dynamics Modeling of Newtonian and Non-Newtonian Fluids in Annulus With Pipe Rotation

Mehmet Sorgun, A. Murat Ozbayoglu, M. Evren Ozbayoglu

Publisher: ASME

J. Energy Resour. Technol. May 2015, 137(3): 032901.

Paper No: JERT-14-1096

https://doi.org/10.1115/1.4028694

Published Online: October 21, 2014

View Article titled, Support Vector Regression and Computational Fluid Dynamics Modeling of Newtonian and Non-Newtonian Fluids in Annulus With Pipe Rotation

Topics: Annulus, Computational fluid dynamics, Fluids, Modeling, Non-Newtonian fluids, Pipes, Rotation, Support vector machines, Pressure gradient, Pressure

Journal Articles

A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields

Habib Rostami, Abbas Khaksar Manshad

Publisher: ASME

J. Energy Resour. Technol. June 2014, 136(2): 024502.

Paper No: JERT-13-1210

https://doi.org/10.1115/1.4026917

Published Online: April 9, 2014

View Article titled, A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields

Topics: Artificial neural networks, Drilling, Pipes, Support vector machines, Oil fields

Journal citation

NARROW

Supplemental Material

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

A Comparative Analysis of Exhaust Gas Temperature Based on Machine Learning Models for Aviation Applications

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

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

Predictive Modeling for Rooftop Solar Energy Throughput: A Machine Learning-Based Optimization for Building Energy Demand Scheduling

Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

Committee Machine-Ensemble as a General Paradigm for Accurate Prediction of Bubble Point Pressure of Crude Oil

Estimation of CO 2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

Support Vector Regression and Computational Fluid Dynamics Modeling of Newtonian and Non-Newtonian Fluids in Annulus With Pipe Rotation

A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

This Feature Is Available To Subscribers Only