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Research on calibrating rock mechanical parameters with a statistical method
Research on the modeling of rock mechanics parameters is of great significance to the exploration of oil and gas. The use of logging data with the Kriging interpolation to study rock mechanics parameters has been proven to be effective in reservoir prediction and other oilfield applications and can...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436635/ https://www.ncbi.nlm.nih.gov/pubmed/28545105 http://dx.doi.org/10.1371/journal.pone.0176215 |
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author | Liu, Zhen Guo, Ye Du, Shuheng Wu, Gengyu Pan, Mao |
author_facet | Liu, Zhen Guo, Ye Du, Shuheng Wu, Gengyu Pan, Mao |
author_sort | Liu, Zhen |
collection | PubMed |
description | Research on the modeling of rock mechanics parameters is of great significance to the exploration of oil and gas. The use of logging data with the Kriging interpolation to study rock mechanics parameters has been proven to be effective in reservoir prediction and other oilfield applications and can provide additional data. However, there will sometimes be a great deviation due to the limited samples and the strong heterogeneity of a layer. To solve this problem, a new approach was proposed to calibrate rock mechanical models through the statistical analysis of logging data. A module was developed to calibrate rock mechanics parameters automatically, which was then applied to the Wangyao area of the Ansai oilfield. This method significantly improved the accuracy of rock mechanics modeling. |
format | Online Article Text |
id | pubmed-5436635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54366352017-05-27 Research on calibrating rock mechanical parameters with a statistical method Liu, Zhen Guo, Ye Du, Shuheng Wu, Gengyu Pan, Mao PLoS One Research Article Research on the modeling of rock mechanics parameters is of great significance to the exploration of oil and gas. The use of logging data with the Kriging interpolation to study rock mechanics parameters has been proven to be effective in reservoir prediction and other oilfield applications and can provide additional data. However, there will sometimes be a great deviation due to the limited samples and the strong heterogeneity of a layer. To solve this problem, a new approach was proposed to calibrate rock mechanical models through the statistical analysis of logging data. A module was developed to calibrate rock mechanics parameters automatically, which was then applied to the Wangyao area of the Ansai oilfield. This method significantly improved the accuracy of rock mechanics modeling. Public Library of Science 2017-05-18 /pmc/articles/PMC5436635/ /pubmed/28545105 http://dx.doi.org/10.1371/journal.pone.0176215 Text en © 2017 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liu, Zhen Guo, Ye Du, Shuheng Wu, Gengyu Pan, Mao Research on calibrating rock mechanical parameters with a statistical method |
title | Research on calibrating rock mechanical parameters with a statistical method |
title_full | Research on calibrating rock mechanical parameters with a statistical method |
title_fullStr | Research on calibrating rock mechanical parameters with a statistical method |
title_full_unstemmed | Research on calibrating rock mechanical parameters with a statistical method |
title_short | Research on calibrating rock mechanical parameters with a statistical method |
title_sort | research on calibrating rock mechanical parameters with a statistical method |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436635/ https://www.ncbi.nlm.nih.gov/pubmed/28545105 http://dx.doi.org/10.1371/journal.pone.0176215 |
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