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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models

Abstract Predicting the rate of penetration (ROP) is critical for optimizing drilling performance, yet it remains a complex task due to the interplay of multiple geological and operational parameters. This study comprehensively evaluates machine learning models, utilizing a real-time, high-resolutio...

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Principais autores: Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi
Format: Artigo
Sprog:Inglês
Udgivet: SpringerOpen 2025-06-01
Serier:Journal of Petroleum Exploration and Production Technology
Fag:
Rate of penetration
Machine learning
Drilling
Random Forest
SHAP
Online adgang:https://doi.org/10.1007/s13202-025-02018-3
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