Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Boris Belozerov"'
Autor:
Denis Volkhonskiy, Ekaterina Muravleva, Oleg Sudakov, Denis Orlov, Evgeny Burnaev, Dmitry Koroteev, Boris Belozerov, Vladislav Krutko
Publikováno v:
Physical review. E. 105(2-2)
In many branches of earth sciences, the problem of rock study on the microlevel arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a1c650326f252643cdd98fe15b851b2
https://pubmed.ncbi.nlm.nih.gov/35291138
https://pubmed.ncbi.nlm.nih.gov/35291138
Publikováno v:
Day 1 Tue, October 22, 2019.
The new approach for production prediction was developed and is described in the article which involves the clustering analysis aimed to well logs such that the reservoir and non-reservoir rocks are obtained (presented by various clusters) and the su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::140a40f8a383f441856e8dd99920786b
https://doi.org/10.2118/196857-ms
https://doi.org/10.2118/196857-ms
Autor:
Ivan Yakimchuk, Nikolay Evseev, Dmitry Korobkov, Igor Varfolomeev, Oleg Dinariev, Vladimir Khan, Dmitry Koroteev, Denis Orlov, Ekaterina Muravleva, Boris Belozerov, Vladislav Krutko, Artem Kondratev
Publikováno v:
Day 3 Thu, October 24, 2019.
The first application of digital rock technology (digital core analysis) with direct hydrodynamic modeling based on density functional theory for the Achimov formation is described. The technology consists of imaging core samples by X-ray microtomogr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8440d3011cb74b954bc9f146e21cd50a
https://doi.org/10.2118/196928-ms
https://doi.org/10.2118/196928-ms
Autor:
Boris Belozerov, Nikita Bukhanov, Dmitry Egorov, Adel Zakirov, Oksana Osmonalieva, Maria Golitsyna, Alexander Reshytko, Artyom Semenikhin, Evgeny Shindin, Vladimir Lipets
Publikováno v:
Day 1 Mon, October 15, 2018.
This paper is devoted to the testing of of automatic well logs interpretation testing, developed on the basis of machine learning methods. The basis of the method presented in the paper is recurrent artificial neural networks. For their training and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e377bfe6b545ab7c7ad660b1d5930e42
https://doi.org/10.2118/191604-18rptc-ms
https://doi.org/10.2118/191604-18rptc-ms
Autor:
Semen Budennyy, Alexey Pachezhertsev, Alexander Bukharev, Artem Erofeev, Dmitry Mitrushkin, Boris Belozerov
Publikováno v:
SPE Russian Petroleum Technology Conference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ae1e3ca07b3e5078a65a3409a3bd5f7
https://doi.org/10.2118/187885-ru
https://doi.org/10.2118/187885-ru