PREDICTION OF DRILL PIPE STUCK BY IMPLEMENTING OF ARTIFICIAL NEURAL NETWORKS

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dc.contributor.authorKizayev, Talgat
dc.date.accessioned2022-07-13T06:25:42Z
dc.date.available2022-07-13T06:25:42Z
dc.date.issued2022-04-13
dc.description.abstractConsidered to be conservative, the oil and gas industry, especially automation in the sector drilling sphere is encountering an extensive metamorphosis charged by new approaches of engineers thinking and digital innovations. The drilling part in the petroleum industry is very extortionate, especially considering the rate of unproductive time due to drill string stuck cases. Nevertheless, due to subject’s input, the lion's share of the research papers gives off the impression of being dispersed. In more detail, it regards the disadvantages of the existing statistical methods compared to machine learning algorithms in terms of stuck pipe prediction during drilling operations in petroleum fields, therefore creating an illusion of fragmentation. The primary purpose of this research is to determine parameters influences in the pipe stuck accidents 11oost model and to analyze the minimum iteration number of stuck pipe using neural network...en_US
dc.identifier.citationKIZAYEV, T. (2022). PREDICTION OF DRILL PIPE STUCK BY IMPLEMENTING OF ARTIFICIAL NEURAL NETWORKS (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstanen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6412
dc.language.isoenen_US
dc.publisherNazarbayev University School of Mining and Geosciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectNeural Networksen_US
dc.subjectCNNen_US
dc.subjectconvolutional neural networksen_US
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.subjectType of access: Gated Accessen_US
dc.titlePREDICTION OF DRILL PIPE STUCK BY IMPLEMENTING OF ARTIFICIAL NEURAL NETWORKSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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