Using machine learning methods for prediction of drilling rate: case of water drilling operations in gneiss rock

<p>An accurate model for ROP prediction in gneissic rock formations was proposed in this study to help reduce drill costs, total drilling time, and allowing companies to be more competitive. The developed ROP prediction model has considered for (04) parameters: percussion pressure, blowing pre...

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Bibliographic Details
Main Author:	Gatchouessi Kamdem Eugène (20527175) (author)
Other Authors:	Kamgue Tiam Franck Ferry (20527178) (author), Mambou Ngueyep Luc Leroy (20527181) (author), Wounabaissa Olivier (20527184) (author), Lembo Nnomo Hugues Richard (20527187) (author), Kanmogne Abraham (20527190) (author)
Published:	2025
Subjects:	Biophysics Medicine Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified ROP prediction hydraulic drilling operations gneissic rocks formations drilling costs artificial intelligence random forest K-Nearest Neighbors regression multilayer perceptron
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Using machine learning methods for prediction of drilling rate: case of water drilling operations in gneiss rock

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