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...
| Main Authors: | Khan, J.A., Irfan, M., Irawan, S., Yao, F.K., Shokor Abdul Rahaman, Md., Shahari, A.R., Glowacz, A., Zeb, N. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.23418 / |
| Published: |
MDPI AG
2020
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089625518&doi=10.3390%2fen13143683&partnerID=40&md5=c49379eb3a6e38b1e98165e362a87388 http://eprints.utp.edu.my/23418/ |
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