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Please use this identifier to cite or link to this item: http://dspace.bsu.edu.ru/handle/123456789/63982
Title: Machine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimization
Authors: Osipov, A.
Pleshakova, E.
Bykov, A.
Kuzichkin, O.
Surzhik, D.
Keywords: technique
mining
drilling optimization
robotics
artificial intelligence
neural networks
engineering
CapsNet
geophysical monitoring
Issue Date: 2023
Citation: Machine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimization / A. Osipov, E. Pleshakova, A. Bykov [et al.] // IEEE Access. - 2023. - Vol.11.-P. 60349-60364. - Refer.: p. 60362-60364.
Abstract: The purpose of the article is to create an effective method to monitor the state of the drill string and the bit without interfering with the drilling process itself in low-time delay mode. For continuous monitoring of the well drilling process, an experimental setup was developed that operates on the basis of the use of the phase-metric method of control. Any movement of the bit causes a change in the electrical characteristics of the probing signal
URI: http://dspace.bsu.edu.ru/handle/123456789/63982
Appears in Collections:Статьи из периодических изданий и сборников (на иностранных языках) = Articles from periodicals and collections (in foreign languages)

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