Neural geological-genetic and radiogeochemical forecast model of oil-bearing fields; IOP Conference Series: Materials Science and Engineering; Vol. 81 : Radiation-Thermal Effects and Processes in Inorganic Materials

Detaylı Bibliyografya
Parent link:IOP Conference Series: Materials Science and Engineering
Vol. 81 : Radiation-Thermal Effects and Processes in Inorganic Materials.— 2015.— [012106, 7 p.]
Yazar: Gorbachev S. V.
Müşterek Yazar: Национальный исследовательский Томский политехнический университет (ТПУ) Институт неразрушающего контроля (ИНК) Кафедра физических методов и приборов контроля качества (ФМПК)
Diğer Yazarlar: Kurkan I. K. Ivan Konstantinovich
Özet:Title screen
In recent years, oil and gas exploration are increasingly turning to direct methods to identify accumulations of hydrocarbons (magnetometry, radiometry, geochemical methods, etc.). Similar works are tested high in the Tomsk region, near the Ob basin. In this paper we present some results of testing of geological and genetic models and radiogeochemical occurrence of hydrocarbons in relation to various oil and gas complexes, with the development of neural network methods of analysis and forecasting, formulated proposals for their integrated use.
Режим доступа: по договору с организацией-держателем ресурса
Dil:İngilizce
Baskı/Yayın Bilgisi: 2015
Konular:
Online Erişim:http://dx.doi.org/10.1088/1757-899X/81/1/012106
http://earchive.tpu.ru/handle/11683/14750
Materyal Türü: Elektronik Kitap Bölümü
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=643042
Diğer Bilgiler
Özet:Title screen
In recent years, oil and gas exploration are increasingly turning to direct methods to identify accumulations of hydrocarbons (magnetometry, radiometry, geochemical methods, etc.). Similar works are tested high in the Tomsk region, near the Ob basin. In this paper we present some results of testing of geological and genetic models and radiogeochemical occurrence of hydrocarbons in relation to various oil and gas complexes, with the development of neural network methods of analysis and forecasting, formulated proposals for their integrated use.
Режим доступа: по договору с организацией-держателем ресурса
DOI:10.1088/1757-899X/81/1/012106