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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

Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest

Hany Osman, Abdulwahab Ali, Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny

Publisher: ASME

J. Energy Resour. Technol. September 2021, 143(9): 093003.

Paper No: JERT-20-2001

https://doi.org/10.1115/1.4050778

Published Online: April 29, 2021

View Article titled, Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest

Topics: Artificial neural networks, Drilling, Machine learning, Regression models, Algorithms, Testing

Journal Articles

Using Machine Learning Tools to Predict Compressor Stall

Samuel M. Hipple, Harry Bonilla-Alvarado, Paolo Pezzini, Lawrence Shadle, Kenneth M. Bryden

Publisher: ASME

J. Energy Resour. Technol. July 2020, 142(7): 070915.

Paper No: JERT-19-1798

https://doi.org/10.1115/1.4046458

Published Online: April 8, 2020

View Article titled, Using Machine Learning Tools to Predict Compressor Stall

Topics: Compressors, Machine learning, Surges, Turbines, Turbomachinery, Stall inception, Regression models, Gas turbines, Artificial neural networks

Journal Articles

Characterization and Prediction of Gas Breakthrough With Cyclic Steam and Gas Stimulation Technique in an Offshore Heavy Oil Reservoir

Haijun Wu, Qingjun Du, Jian Hou, Jingsong Li, Ruxiang Gong, Yongge Liu, Zhitao Li

Publisher: ASME

J. Energy Resour. Technol. May 2017, 139(3): 032801.

Paper No: JERT-14-1177

https://doi.org/10.1115/1.4034857

Published Online: October 13, 2016

View Article titled, Characterization and Prediction of Gas Breakthrough With Cyclic Steam and Gas Stimulation Technique in an Offshore Heavy Oil Reservoir

Topics: Regression models, Reservoirs, Steam, Wells, Permeability, Calibration, Pressure, Hydrocarbon reservoirs

Journal Articles

Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors

Angshuman Deka, Nima Hamta, Behzad Esmaeilian, Sara Behdad

Publisher: ASME

J. Energy Resour. Technol. March 2016, 138(2): 022001.

Paper No: JERT-15-1194

https://doi.org/10.1115/1.4031632

Published Online: October 15, 2015

View Article titled, Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors

Topics: Artificial neural networks, Package on package, Regression models, Energy consumption, Modeling

Journal Articles

Prediction of Slug Liquid Holdup in Horizontal Pipes

Eissa Al-Safran

Publisher: ASME

J. Energy Resour. Technol. June 2009, 131(2): 023001.

https://doi.org/10.1115/1.3120305

Published Online: May 19, 2009

View Article titled, Prediction of Slug Liquid Holdup in Horizontal Pipes

Topics: Pipes, Slug flows, Momentum, Liquid films, Regression models, Errors

Journal Articles

Energy Management Techniques for Small- and Medium-Sized Companies (ESDA2006-95808)

Elena Giacone, Salvatore Mancò, Pietro Gabriele

Publisher: ASME

J. Energy Resour. Technol. March 2008, 130(1): 012002.

https://doi.org/10.1115/1.2835614

Published Online: February 4, 2008

View Article titled, Energy Management Techniques for Small- and Medium-Sized Companies (ESDA2006-95808)

Topics: Energy consumption, Energy management, Quality control charts, Regression analysis, Statistical process control, Energy management systems, Regression models

Journal Articles

An Experimental Investigation of Wood Combustion

A. Dadkhah-Nikoo, D. J. Bushnell

Publisher: ASME

J. Energy Resour. Technol. September 1994, 116(3): 186–193.

https://doi.org/10.1115/1.2906442

Published Online: September 1, 1994

View Article titled, An Experimental Investigation of Wood Combustion

Topics: Combustion, Wood products, Particulate matter, Emissions, Fuels, Temperature, Design, Fire, Particle size, Stress

Journal citation

NARROW

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

Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest

Using Machine Learning Tools to Predict Compressor Stall

Characterization and Prediction of Gas Breakthrough With Cyclic Steam and Gas Stimulation Technique in an Offshore Heavy Oil Reservoir

Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors

Prediction of Slug Liquid Holdup in Horizontal Pipes

Energy Management Techniques for Small- and Medium-Sized Companies (ESDA2006-95808)

An Experimental Investigation of Wood Combustion

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