Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters

Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters

Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. ROP decreases drilling time and costs and increases rig productivity. This study employs random forest (RF), gradient boosting modeling (GBM), extreme gradient boosting (XGBoost), and adaptive boosting (Adaboost...

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Bibliographic Details
Main Authors: Raed H. Allawi, Watheq J. Al-Mudhafar, Mohammed A. Abbas, David A. Wood
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2025-06-01
Series:Artificial Intelligence in Geosciences
Subjects:
Drilling rate of penetration
Ensemble machine learning
Predictive models
Drilling optimization
Drilling/petrophysical inputs
Online Access:http://www.sciencedirect.com/science/article/pii/S2666544125000176
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