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) |
File | Description | Size | Format | |
---|---|---|---|---|
Kuzichkin_Machine_23.pdf | 1.24 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.