Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model ai...

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Bibliographic Details
Main Authors: Javed Akbar Khan, Muhammad Irfan, Sonny Irawan, Fong Kam Yao, Md Shokor Abdul Rahaman, Ahmad Radzi Shahari, Adam Glowacz, Nazia Zeb
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Energies
Subjects:
artificial neural networks
drilling operation
machine learning classifiers
RBF Kernel function
stuck pipe
support vector machines
Online Access:https://www.mdpi.com/1996-1073/13/14/3683

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