Bazhenov Fm Classification Based on Wireline Logs

Bazhenov Fm Classification Based on Wireline Logs

Detalhes bibliográficos
Parent link:IOP Conference Series: Earth and Environmental Science
Vol. 33 : Contemporary Issues of Hydrogeology, Engineering Geology and Hydrogeoecology in Eurasia.— 2016.— [012034, 5 p.]
Autor principal: Simonov D. A. Dmitry Arturovich
Autor Corporativo: Национальный исследовательский Томский политехнический университет (ТПУ) Институт природных ресурсов (ИПР) Центр подготовки и переподготовки специалистов нефтегазового дела (ЦППС НД) Лаборатория геологии месторождений нефти и газа (ЛГМНГ)
Outros Autores: Baranov V. E. Vitaliy Evgenievich, Bukhanov N. V. Nikita Vladimirovich
Resumo:Title screen
This paper considers the main aspects of Bazhenov Formation interpretation and application of machine learning algorithms for the Kolpashev type section of the Bazhenov Formation, application of automatic classification algorithms that would change the scale of research from small to large. Machine learning algorithms help interpret the Bazhenov Formation in a reference well and in other wells. During this study, unsupervised and supervised machine learning algorithms were applied to interpret lithology and reservoir properties. This greatly simplifies the routine problem of manual interpretation and has an economic effect on the cost of laboratory analysis.
Publicado em: 2016
Colecção:Well drilling
Assuntos:
электронный ресурс
труды учёных ТПУ
каротажные диаграммы
баженовская свита
машинное обучение
скважины
лабораторный анализ
Acesso em linha:http://dx.doi.org/10.1088/1755-1315/33/1/012034
http://earchive.tpu.ru/handle/11683/33992
Formato: Recurso Electrónico Capítulo de Livro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=649495

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