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Please use this identifier to cite or link to this item: http://dspace.bsu.edu.ru/handle/123456789/63982
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dc.contributor.authorOsipov, A.-
dc.contributor.authorPleshakova, E.-
dc.contributor.authorBykov, A.-
dc.contributor.authorKuzichkin, O.-
dc.contributor.authorSurzhik, D.-
dc.date.accessioned2024-11-22T07:51:55Z-
dc.date.available2024-11-22T07:51:55Z-
dc.date.issued2023-
dc.identifier.citationMachine 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.urihttp://dspace.bsu.edu.ru/handle/123456789/63982-
dc.description.abstractThe 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 signalru
dc.language.isoenru
dc.subjecttechniqueru
dc.subjectminingru
dc.subjectdrilling optimizationru
dc.subjectroboticsru
dc.subjectartificial intelligenceru
dc.subjectneural networksru
dc.subjectengineeringru
dc.subjectCapsNetru
dc.subjectgeophysical monitoringru
dc.titleMachine Learning Methods Based on Geophysical Monitoring Data in Low Time Delay Mode for Drilling Optimizationru
dc.typeArticleru
Appears in Collections:Статьи из периодических изданий и сборников (на иностранных языках) = Articles from periodicals and collections (in foreign languages)

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