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Keywords: random forest
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Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. August 2023, 145(8): 083301.
Paper No: JERT-22-1951
Published Online: March 10, 2023
... on a large amount of original geological, engineering, and dynamic data acquired from 373 hydraulically fractured horizontal wells, the flowback characteristics were systematically studied based on machine learning. Based on the data analysis and random forest forecasting, a new indicator, single-cluster...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. June 2023, 145(6): 062302.
Paper No: JERT-22-1806
Published Online: January 9, 2023
... discrete element method (DEM) approach to investigate the velocity and solid residence time distributions in the moving bed. In this work, the flow patterns under different operating and structural parameters are studied and optimized via machine learning methods. The random Forest regression model...
Journal Articles
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. April 2022, 144(4): 043203.
Paper No: JERT-21-1527
Published Online: July 16, 2021
... of compressional and shear slowness (ΔT c and ΔT s ) are considered costly and time-consuming operations. The target of this paper is to propose machine learning models for predicting the sonic logs from the drilling data in real-time. Decision tree (DT) and random forest (RF) were employed as train-based...
Journal Articles
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
... the structural relationships existing between the inputs and target variables; these techniques were recently successfully applied to estimate the ROP in different wellbore shapes and through various formation lithologies. This study is aimed to introduce a random forest (RF) regression model for ROP prediction...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. April 2021, 143(4): 043201.
Paper No: JERT-20-1090
Published Online: August 28, 2020
...-in. horizontal section until 1 day prior to the stuck pipe incident, were used to train and test three models: random forest, artificial neural network, and functional network, with an 80/20 training-to-testing data ratio, to predict the surface drilling torque. The independent variables for the model...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. July 2020, 142(7): 070908.
Paper No: JERT-19-1511
Published Online: February 24, 2020
... is that it requires less formal statistical training and its ability to implicitly detect complex non-linear relationships between dependent and independent variables. 2 In this work, a few machine learning algorithms were compared in order to find the best model for failure prediction: Random Forest, Gradient...