Application of BigData technology to improve the efficiency of Arctic shelf fields development
Abstract Development processes in the Arctic zone require that a set of tasks related to the development or improvement of technologies, as well as to the optimization of project management methods be solved. It has been noted that in order to solve the tasks, fast updated Big Data is needed, the ti...
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Online Access: | http://dx.doi.org/10.1088/1755-1315/937/4/042080 https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042080 https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042080/pdf |
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crioppubl:10.1088/1755-1315/937/4/042080 2024-06-02T08:01:04+00:00 Application of BigData technology to improve the efficiency of Arctic shelf fields development Katysheva, E G 2021 http://dx.doi.org/10.1088/1755-1315/937/4/042080 https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042080 https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042080/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining IOP Conference Series: Earth and Environmental Science volume 937, issue 4, page 042080 ISSN 1755-1307 1755-1315 journal-article 2021 crioppubl https://doi.org/10.1088/1755-1315/937/4/042080 2024-05-07T14:00:15Z Abstract Development processes in the Arctic zone require that a set of tasks related to the development or improvement of technologies, as well as to the optimization of project management methods be solved. It has been noted that in order to solve the tasks, fast updated Big Data is needed, the timely acquisition and processing of which will allow for unbiased assessment of the current situation, taking appropriate management decisions, and prompt adjusting as new factors arise. It has been concluded that the introduction of Big Data technology is considered to be the most efficient Industry 4.0 tool for geological survey, and data arrays on the state of exploration of the territories and the results of exploration drilling can serve as the basis for an information model of oil and gas exploration. It has also been found that the array accumulated by subsoil users in the course of scientific research makes it possible to significantly increase the state of exploration of the natural Arctic environment and assess in an unbiased manner the natural processes that occur in the areas of the northern seas. Based on the analysis of the collected data, to predict the state of the natural environment and further develop optimal technical and managerial solutions for the development of the Arctic fields is possible. Article in Journal/Newspaper Arctic IOP Publishing Arctic IOP Conference Series: Earth and Environmental Science 937 4 042080 |
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Abstract Development processes in the Arctic zone require that a set of tasks related to the development or improvement of technologies, as well as to the optimization of project management methods be solved. It has been noted that in order to solve the tasks, fast updated Big Data is needed, the timely acquisition and processing of which will allow for unbiased assessment of the current situation, taking appropriate management decisions, and prompt adjusting as new factors arise. It has been concluded that the introduction of Big Data technology is considered to be the most efficient Industry 4.0 tool for geological survey, and data arrays on the state of exploration of the territories and the results of exploration drilling can serve as the basis for an information model of oil and gas exploration. It has also been found that the array accumulated by subsoil users in the course of scientific research makes it possible to significantly increase the state of exploration of the natural Arctic environment and assess in an unbiased manner the natural processes that occur in the areas of the northern seas. Based on the analysis of the collected data, to predict the state of the natural environment and further develop optimal technical and managerial solutions for the development of the Arctic fields is possible. |
format |
Article in Journal/Newspaper |
author |
Katysheva, E G |
spellingShingle |
Katysheva, E G Application of BigData technology to improve the efficiency of Arctic shelf fields development |
author_facet |
Katysheva, E G |
author_sort |
Katysheva, E G |
title |
Application of BigData technology to improve the efficiency of Arctic shelf fields development |
title_short |
Application of BigData technology to improve the efficiency of Arctic shelf fields development |
title_full |
Application of BigData technology to improve the efficiency of Arctic shelf fields development |
title_fullStr |
Application of BigData technology to improve the efficiency of Arctic shelf fields development |
title_full_unstemmed |
Application of BigData technology to improve the efficiency of Arctic shelf fields development |
title_sort |
application of bigdata technology to improve the efficiency of arctic shelf fields development |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
http://dx.doi.org/10.1088/1755-1315/937/4/042080 https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042080 https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042080/pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
IOP Conference Series: Earth and Environmental Science volume 937, issue 4, page 042080 ISSN 1755-1307 1755-1315 |
op_rights |
http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1755-1315/937/4/042080 |
container_title |
IOP Conference Series: Earth and Environmental Science |
container_volume |
937 |
container_issue |
4 |
container_start_page |
042080 |
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1800745323720605696 |