Assessing geologic model uncertainty : a case study comparing methods
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/232736 |
Resumo: | Evaluating mineral resources requires the prior delimitation of geologically homogeneous stationary domains. The knowledge about the ore genesis and geological processes involved are translated into three dimensional models, essential for planning the production and decision-making. The mineral industry usually considers grade uncertainty for resource evaluation; however, uncertainty related to the geological boundaries are often neglected. This uncertainty, related to the location of the boundary between distinct geological domains can be one of the major sources of uncertainty in a mineral project, and should be assessed due to its potential impact on the ore tonnage, and consequently, on enterprise profitability. This study aims at presenting three different methodologies capable of generating multiple geomodel realizations and thus, assessing uncertainty. A real dataset with high geological complexity is used to illustrate the methodology. The results are compared to a deterministic model used as a reference scenario. |
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Amarante, Flávio Azevedo NevesRolo, Roberto MentzingenCosta, Joao Felipe Coimbra Leite2021-12-09T04:35:54Z20192448-167Xhttp://hdl.handle.net/10183/232736001133791Evaluating mineral resources requires the prior delimitation of geologically homogeneous stationary domains. The knowledge about the ore genesis and geological processes involved are translated into three dimensional models, essential for planning the production and decision-making. The mineral industry usually considers grade uncertainty for resource evaluation; however, uncertainty related to the geological boundaries are often neglected. This uncertainty, related to the location of the boundary between distinct geological domains can be one of the major sources of uncertainty in a mineral project, and should be assessed due to its potential impact on the ore tonnage, and consequently, on enterprise profitability. This study aims at presenting three different methodologies capable of generating multiple geomodel realizations and thus, assessing uncertainty. A real dataset with high geological complexity is used to illustrate the methodology. The results are compared to a deterministic model used as a reference scenario.application/pdfengREM : international engineering journal. Ouro Preto, MG. Vol. 72, no. 4 (Oct./Dec. 2019), p. 643-653Modelo geológicoGeoestatísticaIncertezaGeological modelMultipoint geostatisticsImplicit modelingUncertaintyAssessing geologic model uncertainty : a case study comparing methodsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001133791.pdf.txt001133791.pdf.txtExtracted Texttext/plain45332http://www.lume.ufrgs.br/bitstream/10183/232736/2/001133791.pdf.txt30ee900c4b90562db5c7cbbf58f051fbMD52ORIGINAL001133791.pdfTexto completo (inglês)application/pdf4282385http://www.lume.ufrgs.br/bitstream/10183/232736/1/001133791.pdf133d759459a772f1286fcd1846e9fc25MD5110183/2327362021-12-19 05:30:20.621418oai:www.lume.ufrgs.br:10183/232736Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-12-19T07:30:20Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Assessing geologic model uncertainty : a case study comparing methods |
title |
Assessing geologic model uncertainty : a case study comparing methods |
spellingShingle |
Assessing geologic model uncertainty : a case study comparing methods Amarante, Flávio Azevedo Neves Modelo geológico Geoestatística Incerteza Geological model Multipoint geostatistics Implicit modeling Uncertainty |
title_short |
Assessing geologic model uncertainty : a case study comparing methods |
title_full |
Assessing geologic model uncertainty : a case study comparing methods |
title_fullStr |
Assessing geologic model uncertainty : a case study comparing methods |
title_full_unstemmed |
Assessing geologic model uncertainty : a case study comparing methods |
title_sort |
Assessing geologic model uncertainty : a case study comparing methods |
author |
Amarante, Flávio Azevedo Neves |
author_facet |
Amarante, Flávio Azevedo Neves Rolo, Roberto Mentzingen Costa, Joao Felipe Coimbra Leite |
author_role |
author |
author2 |
Rolo, Roberto Mentzingen Costa, Joao Felipe Coimbra Leite |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Amarante, Flávio Azevedo Neves Rolo, Roberto Mentzingen Costa, Joao Felipe Coimbra Leite |
dc.subject.por.fl_str_mv |
Modelo geológico Geoestatística Incerteza |
topic |
Modelo geológico Geoestatística Incerteza Geological model Multipoint geostatistics Implicit modeling Uncertainty |
dc.subject.eng.fl_str_mv |
Geological model Multipoint geostatistics Implicit modeling Uncertainty |
description |
Evaluating mineral resources requires the prior delimitation of geologically homogeneous stationary domains. The knowledge about the ore genesis and geological processes involved are translated into three dimensional models, essential for planning the production and decision-making. The mineral industry usually considers grade uncertainty for resource evaluation; however, uncertainty related to the geological boundaries are often neglected. This uncertainty, related to the location of the boundary between distinct geological domains can be one of the major sources of uncertainty in a mineral project, and should be assessed due to its potential impact on the ore tonnage, and consequently, on enterprise profitability. This study aims at presenting three different methodologies capable of generating multiple geomodel realizations and thus, assessing uncertainty. A real dataset with high geological complexity is used to illustrate the methodology. The results are compared to a deterministic model used as a reference scenario. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019 |
dc.date.accessioned.fl_str_mv |
2021-12-09T04:35:54Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/232736 |
dc.identifier.issn.pt_BR.fl_str_mv |
2448-167X |
dc.identifier.nrb.pt_BR.fl_str_mv |
001133791 |
identifier_str_mv |
2448-167X 001133791 |
url |
http://hdl.handle.net/10183/232736 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
REM : international engineering journal. Ouro Preto, MG. Vol. 72, no. 4 (Oct./Dec. 2019), p. 643-653 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
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UFRGS |
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Repositório Institucional da UFRGS |
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Repositório Institucional da UFRGS |
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