Comparative analysis of machine learning techniques for predicting drilling rate of penetration (ROP) in geothermal wells: A case study of FORGE site-Reference-Cited by-同舟云学术

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Comparative analysis of machine learning techniques for predicting drilling rate of penetration (ROP) in geothermal wells: A case study of FORGE site

Published:2024-07 Issue: Volume:121 Page:103028
ISSN:0375-6505
Container-title:Geothermics
language:en
Short-container-title:Geothermics

Author:

Yehia Taha^ORCID,Gasser Moamen^ORCID,Ebaid Hossam^ORCID,Meehan Nathan,Okoroafor Esuru Rita^ORCID

Publisher

Elsevier BV

Reference67 articles.

1. Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance;Ahsan;Technologies,2021

2. Ali, M., 2020. PyCaret: an open source, low-code machine learning library in python.

3. An introduction to kernel and nearest-neighbor nonparametric regression;Altman;Am Stat,1992

4. Applying machine learning to predict the rate of penetration for geothermal drilling located in the Utah FORGE site;Ben Aoun;Energies,2022

5. A random forest guided tour;Biau;TEST,2015

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