| Поле DC | Значение | Язык |
|---|
| dc.contributor.author | Osipov, A. | - |
| dc.contributor.author | Pleshakova, E. | - |
| dc.contributor.author | Bykov, A. | - |
| dc.contributor.author | Kuzichkin, O. | - |
| dc.contributor.author | Surzhik, D. | - |
| dc.date.accessioned | 2024-11-22T07:51:55Z | - |
| dc.date.available | 2024-11-22T07:51:55Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.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. | ru |
| dc.identifier.uri | http://dspace.bsu.edu.ru/handle/123456789/63982 | - |
| dc.description.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 | ru |
| dc.language.iso | en | ru |
| dc.subject | technique | ru |
| dc.subject | mining | ru |
| dc.subject | drilling optimization | ru |
| dc.subject | robotics | ru |
| dc.subject | artificial intelligence | ru |
| dc.subject | neural networks | ru |
| dc.subject | engineering | ru |
| dc.subject | CapsNet | ru |
| dc.subject | geophysical monitoring | ru |
| dc.title | Machine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimization | ru |
| dc.type | Article | ru |
| Располагается в коллекциях: | Статьи из периодических изданий и сборников (на иностранных языках) = Articles from periodicals and collections (in foreign languages)
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