Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site

Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site

Well planning for every drilling project includes cost estimation. Maximizing the rate of penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses required for the drilling budget. The empirical formulas developed to predict ROP have limited field applications. Si...

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Bibliographic Details
Main Authors: Mohamed Arbi Ben Aoun, Tamás Madarász
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Energies
Subjects:
rate of penetration (ROP)
predictive modeling
geothermal energy
machine learning
deep learning
random forests
Online Access:https://www.mdpi.com/1996-1073/15/12/4288

Internet

https://www.mdpi.com/1996-1073/15/12/4288

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