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Methods of automation the process of detailed correlation of well sections with the use of machine learning

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dc.contributor.author Murtazin T.
dc.contributor.author Ismagilov A.
dc.contributor.author Novikiva S.
dc.contributor.author Sudakov V.
dc.date.accessioned 2020-01-15T21:46:46Z
dc.date.available 2020-01-15T21:46:46Z
dc.date.issued 2019
dc.identifier.issn 1314-2704
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/155862
dc.description.abstract © SGEM2019. When constructing a geological model at the stage of 3D grid building, the problem of vertical layering. When modeling is performed on the area with a large number of wells, the process of cross-well correlation of productive strata turns into a very labor consuming stage. To speed up and increase the reliability of the correlation of well sections, the authors proposed an approach to automate this time-consuming process. This approach is based on the use of machine learning algorithms that can reproduce the nonlinear part of the logic of the interwell correlation procedure. In addition to the automatic algorithms, the user friendly interface was created to simplify the procedure of control and editing the results. As a result, the authors have built a program code including the automatic mode to solve the problem of interwell detailed correlation of productive layers. The proposed solution algorithm was tested on data from one of the Tatarstan Republic oilfields and showed a high degree of convergence with the results obtained by expert.
dc.relation.ispartofseries International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM
dc.subject Detailed correlation
dc.subject Geological model
dc.subject Machine learning
dc.title Methods of automation the process of detailed correlation of well sections with the use of machine learning
dc.type Conference Paper
dc.relation.ispartofseries-issue 1.1
dc.relation.ispartofseries-volume 19
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 311
dc.source.id SCOPUS13142704-2019-19-11-SID85073696497


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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