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Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning

In: ICT for an Inclusive World

Author

Listed:
  • Djamil Rezki

    (Batna 2 University)

  • Leila Hayet Mouss

    (Batna 2 University)

  • Abdelkader Baaziz

    (Institut Méditerranéen des Sciences de l’Information et de la Communication (IMSIC) — Université Aix-Marseille)

  • Nafissa Rezki

    (Batna 2 University)

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.

Suggested Citation

  • Djamil Rezki & Leila Hayet Mouss & Abdelkader Baaziz & Nafissa Rezki, 2020. "Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning," Lecture Notes in Information Systems and Organization, in: Youcef Baghdadi & Antoine Harfouche & Marta Musso (ed.), ICT for an Inclusive World, pages 537-549, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-34269-2_37
    DOI: 10.1007/978-3-030-34269-2_37
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