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Keywords: machine learning
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Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. November 2022, 144(11): 112104.
Paper No: JERT-21-1590
Published Online: April 26, 2022
... the above experimentally observed non-monotonous viscosity behavior, various machine learning models were employed; support vector regression (SVR)based models predicted the slickwater fluid viscosity with maximum accuracy. Sensitivity analysis was carried out to determine the prominence of the studied...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. November 2022, 144(11): 112307.
Paper No: JERT-21-1965
Published Online: April 26, 2022
.... The available experimental results of such a modified engine including noise and the test conditions were randomly distributed without careful design. As a result, the machine learning model was utilized to assist in analyzing the ammonia engine performance by reducing the experimental uncertainty. The results...
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. October 2022, 144(10): 102108.
Paper No: JERT-21-1675
Published Online: March 22, 2022
... reduction in the aerospace, automotive, and construction industries. This integrated modeling approach has not been fully applied to nuclear safeguards programs in the past. Digital twinning, combined with machine learning technologies, can lead to new innovations in process-monitoring detection...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. October 2022, 144(10): 103003.
Paper No: JERT-22-1087
Published Online: March 18, 2022
... machine learning approaches, SVM and FN, two models were presented in this work to predict the formation resistivity of subsurface formations. Data were gathered from vertical complex carbonate sections in the Middle East throughout the data collection stage. The data were then cleaned and filtered...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. September 2022, 144(9): 093006.
Paper No: JERT-21-2017
Published Online: March 2, 2022
... and machine learning (AI/ML) has become essential. Unfortunately, due to the harsh environments of drilling and the data-transmission setup, a significant amount of the real-time data could defect. The quality and effectiveness of AI/ML models are directly related to the quality of the input data; only...
Journal Articles
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. March 2022, 144(3): 032310.
Paper No: JERT-21-1797
Published Online: January 7, 2022
...Jinlong Liu; Qiao Huang; Christopher Ulishney; Cosmin E. Dumitrescu Machine learning (ML) models can accelerate the development of efficient internal combustion engines. This study assessed the feasibility of data-driven methods toward predicting the performance of a diesel engine modified...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. January 2022, 144(1): 013003.
Paper No: JERT-21-1741
Published Online: October 5, 2021
.... Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1131 data points includes GOR, upstream and downstream pressures (PU and PD...
Journal Articles
Publisher: ASME
Article Type: Technical Briefs
J. Energy Resour. Technol. December 2021, 143(12): 124501.
Paper No: JERT-21-1215
Published Online: September 24, 2021
... to collect a large amount of data for data-driven methods development and testing. The main study in this article is to develop machine learning algorithms for identifying abnormal drilling and test these algorithms on the rig based on the responses of the rig sensors in real-time operations. The idea also...
Journal Articles
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. February 2022, 144(2): 023001.
Paper No: JERT-21-1326
Published Online: May 13, 2021
... that the drilling data such as drill pipe torque, weight on bit, and rate of penetration are available at an early stage without additional cost. Three machine learning algorithms were used to correlate the drilling data with Young's modulus: random forest, adaptive neuro-fuzzy inference system, and functional...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. September 2021, 143(9): 093003.
Paper No: JERT-20-2001
Published Online: April 29, 2021
...Hany Osman; Abdulwahab Ali; Ahmed Abdulhamid Mahmoud; Salaheldin Elkatatny Predicting the rate of penetration (ROP) is challenging especially during horizontal drilling. This is because there are many factors affecting ROP. Machine learning techniques are very promising in identifying...
Journal Articles
Journal Articles
Journal Articles
Journal Articles