APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD

The article is devoted to development of methodological techniques for application of machine learning technologies, including deep learning, to the problems of in-depth analysis of geological and physical parameters based on the results of laboratory studies of core sections. To achieve this goal,...

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Published in:Izvestiya Tomskogo Politekhnicheskogo Universiteta Inziniring Georesursov
Main Authors: Nikita A. Popov, Ivan S. Putilov, Anastasiya A. Gulyaeva, Ekaterina V. Vinokurova
Format: Article in Journal/Newspaper
Language:Russian
Published: Tomsk Polytechnic University 2020
Subjects:
Online Access:https://doi.org/10.18799/24131830/2020/6/2681
https://doaj.org/article/7432df2105854e41ab4a36131024ef4e
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spelling ftdoajarticles:oai:doaj.org/article:7432df2105854e41ab4a36131024ef4e 2023-05-15T18:42:27+02:00 APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD Nikita A. Popov Ivan S. Putilov Anastasiya A. Gulyaeva Ekaterina V. Vinokurova 2020-06-01T00:00:00Z https://doi.org/10.18799/24131830/2020/6/2681 https://doaj.org/article/7432df2105854e41ab4a36131024ef4e RU rus Tomsk Polytechnic University http://izvestiya.tpu.ru/archive/article/view/2681/2245 https://doaj.org/toc/2500-1019 https://doaj.org/toc/2413-1830 doi:10.18799/24131830/2020/6/2681 2500-1019 2413-1830 https://doaj.org/article/7432df2105854e41ab4a36131024ef4e Известия Томского политехнического университета: Инжиниринг георесурсов, Vol 331, Iss 6, Pp 100-112 (2020) technology of machine learning investigations of core description of thin sections mathematical-statistical analysis the classification of danhem Engineering geology. Rock mechanics. Soil mechanics. Underground construction TA703-712 article 2020 ftdoajarticles https://doi.org/10.18799/24131830/2020/6/2681 2022-12-31T10:54:36Z The article is devoted to development of methodological techniques for application of machine learning technologies, including deep learning, to the problems of in-depth analysis of geological and physical parameters based on the results of laboratory studies of core sections. To achieve this goal, we solve the problem of developing a specialized tabular format for describing the core sections of carbonate deposits, formation of a database on the basis of the developed format for further analysis and application of deep and surface training technologies. The permocarbon deposit of Usinsk field located in the Komi Republic was chosen as the object of research. Deep learning technology was applied to obtain a mathematical model for predicting a number of geological parameters from the photos of sections. As the main example, the forecast of eight classes of Danhem, allocated by sections, was considered. The developed format allows presenting all text descriptions of the geological characteristics of the section in a tabular form with a discrete encoding. The table view provides a number of advantages. First, it allows you to perform mathematical and statistical analysis of the description of sections. Second, it is possible to form a database for analysis, using the results of the work of different authors, including photographs of thin sections, thirdly, provides an opportunity to compare and analyze the parameters obtained for the sections with other results of studies of the cores. On the example of permocarbon deposit of Usinsk field, a unique database of 500 sections from 6 wells was formed according to the developed format. In addition to the descriptions of the sections, the database was loaded with information on the results of laboratory studies of various geological and physical parameters obtained on standard core samples from the same intervals as the sections. Using the formed database, the ratio of mineralogical density and permeability with the categorization of points according to the Danhem ... Article in Journal/Newspaper Usinsk Directory of Open Access Journals: DOAJ Articles Usinsk ENVELOPE(57.528,57.528,65.994,65.994) Izvestiya Tomskogo Politekhnicheskogo Universiteta Inziniring Georesursov 331 6 100 112
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language Russian
topic technology of machine learning
investigations of core
description of thin sections
mathematical-statistical analysis
the classification of danhem
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
TA703-712
spellingShingle technology of machine learning
investigations of core
description of thin sections
mathematical-statistical analysis
the classification of danhem
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
TA703-712
Nikita A. Popov
Ivan S. Putilov
Anastasiya A. Gulyaeva
Ekaterina V. Vinokurova
APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD
topic_facet technology of machine learning
investigations of core
description of thin sections
mathematical-statistical analysis
the classification of danhem
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
TA703-712
description The article is devoted to development of methodological techniques for application of machine learning technologies, including deep learning, to the problems of in-depth analysis of geological and physical parameters based on the results of laboratory studies of core sections. To achieve this goal, we solve the problem of developing a specialized tabular format for describing the core sections of carbonate deposits, formation of a database on the basis of the developed format for further analysis and application of deep and surface training technologies. The permocarbon deposit of Usinsk field located in the Komi Republic was chosen as the object of research. Deep learning technology was applied to obtain a mathematical model for predicting a number of geological parameters from the photos of sections. As the main example, the forecast of eight classes of Danhem, allocated by sections, was considered. The developed format allows presenting all text descriptions of the geological characteristics of the section in a tabular form with a discrete encoding. The table view provides a number of advantages. First, it allows you to perform mathematical and statistical analysis of the description of sections. Second, it is possible to form a database for analysis, using the results of the work of different authors, including photographs of thin sections, thirdly, provides an opportunity to compare and analyze the parameters obtained for the sections with other results of studies of the cores. On the example of permocarbon deposit of Usinsk field, a unique database of 500 sections from 6 wells was formed according to the developed format. In addition to the descriptions of the sections, the database was loaded with information on the results of laboratory studies of various geological and physical parameters obtained on standard core samples from the same intervals as the sections. Using the formed database, the ratio of mineralogical density and permeability with the categorization of points according to the Danhem ...
format Article in Journal/Newspaper
author Nikita A. Popov
Ivan S. Putilov
Anastasiya A. Gulyaeva
Ekaterina V. Vinokurova
author_facet Nikita A. Popov
Ivan S. Putilov
Anastasiya A. Gulyaeva
Ekaterina V. Vinokurova
author_sort Nikita A. Popov
title APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD
title_short APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD
title_full APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD
title_fullStr APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD
title_full_unstemmed APPLICATION OF DEEP LEARNING TECHNOLOGIES FOR STUDYING THIN SECTIONS ON THE EXAMPLE OF USINSK OIL FIELD
title_sort application of deep learning technologies for studying thin sections on the example of usinsk oil field
publisher Tomsk Polytechnic University
publishDate 2020
url https://doi.org/10.18799/24131830/2020/6/2681
https://doaj.org/article/7432df2105854e41ab4a36131024ef4e
long_lat ENVELOPE(57.528,57.528,65.994,65.994)
geographic Usinsk
geographic_facet Usinsk
genre Usinsk
genre_facet Usinsk
op_source Известия Томского политехнического университета: Инжиниринг георесурсов, Vol 331, Iss 6, Pp 100-112 (2020)
op_relation http://izvestiya.tpu.ru/archive/article/view/2681/2245
https://doaj.org/toc/2500-1019
https://doaj.org/toc/2413-1830
doi:10.18799/24131830/2020/6/2681
2500-1019
2413-1830
https://doaj.org/article/7432df2105854e41ab4a36131024ef4e
op_doi https://doi.org/10.18799/24131830/2020/6/2681
container_title Izvestiya Tomskogo Politekhnicheskogo Universiteta Inziniring Georesursov
container_volume 331
container_issue 6
container_start_page 100
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