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,...
Published in: | Izvestiya Tomskogo Politekhnicheskogo Universiteta Inziniring Georesursov |
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Tomsk Polytechnic University
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Online Access: | https://doi.org/10.18799/24131830/2020/6/2681 https://doaj.org/article/7432df2105854e41ab4a36131024ef4e |
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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 |
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Izvestiya Tomskogo Politekhnicheskogo Universiteta Inziniring Georesursov |
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331 |
container_issue |
6 |
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100 |
op_container_end_page |
112 |
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