@inbook {7140,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	booktitle = {ICT for an Inclusive World},
	year = {2020},
	pages = {537-549},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.},
	url = {https://econpapers.repec.org/bookchap/sprlnichp/978-3-030-34269-2_5f37.htm},
	author = {Rezki, Djamil and Mouss, Leila-Hayet and Baaziz, Abdelkader and Rezki, Nafissa}
}
@article {7167,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	journal = {Lecture Notes in Information Systems and Organisation},
	year = {2020},
	author = {Rezki, Djamil and Mouss, Leila-Hayet and Baaziz, Abdelkader and Rezki, Nafissa}
}
@article {7174,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	journal = {ICT for an Inclusive World},
	year = {2020},
	abstract = {This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-34269-2_37},
	author = {Rezki, Djamil and Leila Hayet Mouss and Baaziz, Abdelkader and Rezki, Nafissa}
}
@inbook {62938,
	title = {Rate of penetration (ROP) prediction in oil drilling based on ensemble machine learning},
	booktitle = {ICT for an Inclusive World},
	year = {2020},
	month = {2020},
	pages = {537-549},
	publisher = {Springer},
	organization = {Springer},
	author = {Rezki, Djamil and Leila Hayet Mouss and Baaziz, Abdelkader and Rezki, Nafissa}
}
