Predicting reservoir quality in sandstones through neural modeling
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da FURG (RI FURG) |
Texto Completo: | http://repositorio.furg.br/handle/1/4242 |
Resumo: | Due to limited understanding of the details of many diagenetic processes, mathematical models become a very useful tool to predict reservoir quality prior to drilling. Porosity prediction is an important component in pre-drill and post-drill evaluation of reservoir quality. In this context, we have developed a mathematical model to predict porosity of sandstones reservoir systems. This model is based on artificial neural networks techniques. We propose a score to quantify their importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regression. The main contribution of this paper is the building of a reduced model just with the most relevant features to porosity prediction. A dataset about Uerê formation sandstone reservoir was investigated. This formation is an important oil exploration target in Solimões Basin, western Brazilian Amazonia. Study results show that progressive enhancement neural network is able to predict porosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction. |
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Predicting reservoir quality in sandstones through neural modelingProgressive enhancement neural modelSandstones reservoir qualityPorosity predictionDue to limited understanding of the details of many diagenetic processes, mathematical models become a very useful tool to predict reservoir quality prior to drilling. Porosity prediction is an important component in pre-drill and post-drill evaluation of reservoir quality. In this context, we have developed a mathematical model to predict porosity of sandstones reservoir systems. This model is based on artificial neural networks techniques. We propose a score to quantify their importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regression. The main contribution of this paper is the building of a reduced model just with the most relevant features to porosity prediction. A dataset about Uerê formation sandstone reservoir was investigated. This formation is an important oil exploration target in Solimões Basin, western Brazilian Amazonia. Study results show that progressive enhancement neural network is able to predict porosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction.2013-11-25T11:59:18Z2013-11-25T11:59:18Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCAMARGO, Sandro da Silva; ENGEL, Paulo Martins. Predicting reservoir quality in sandstones through neural modeling. Vetor, Rio Grande, v. 22, n. 1, p. 57-70, 2012. Disponível em: <http://www.seer.furg.br/vetor/article/view/1337/2140>. Acesso em: nov. 2013.0102-7352http://repositorio.furg.br/handle/1/4242engCamargo, Sandro da SilvaEngel, Paulo Martinsinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FURG (RI FURG)instname:Universidade Federal do Rio Grande (FURG)instacron:FURG2013-11-25T12:00:21Zoai:repositorio.furg.br:1/4242Repositório InstitucionalPUBhttps://repositorio.furg.br/oai/request || http://200.19.254.174/oai/requestopendoar:2013-11-25T12:00:21Repositório Institucional da FURG (RI FURG) - Universidade Federal do Rio Grande (FURG)false |
dc.title.none.fl_str_mv |
Predicting reservoir quality in sandstones through neural modeling |
title |
Predicting reservoir quality in sandstones through neural modeling |
spellingShingle |
Predicting reservoir quality in sandstones through neural modeling Camargo, Sandro da Silva Progressive enhancement neural model Sandstones reservoir quality Porosity prediction |
title_short |
Predicting reservoir quality in sandstones through neural modeling |
title_full |
Predicting reservoir quality in sandstones through neural modeling |
title_fullStr |
Predicting reservoir quality in sandstones through neural modeling |
title_full_unstemmed |
Predicting reservoir quality in sandstones through neural modeling |
title_sort |
Predicting reservoir quality in sandstones through neural modeling |
author |
Camargo, Sandro da Silva |
author_facet |
Camargo, Sandro da Silva Engel, Paulo Martins |
author_role |
author |
author2 |
Engel, Paulo Martins |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Camargo, Sandro da Silva Engel, Paulo Martins |
dc.subject.por.fl_str_mv |
Progressive enhancement neural model Sandstones reservoir quality Porosity prediction |
topic |
Progressive enhancement neural model Sandstones reservoir quality Porosity prediction |
description |
Due to limited understanding of the details of many diagenetic processes, mathematical models become a very useful tool to predict reservoir quality prior to drilling. Porosity prediction is an important component in pre-drill and post-drill evaluation of reservoir quality. In this context, we have developed a mathematical model to predict porosity of sandstones reservoir systems. This model is based on artificial neural networks techniques. We propose a score to quantify their importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regression. The main contribution of this paper is the building of a reduced model just with the most relevant features to porosity prediction. A dataset about Uerê formation sandstone reservoir was investigated. This formation is an important oil exploration target in Solimões Basin, western Brazilian Amazonia. Study results show that progressive enhancement neural network is able to predict porosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012 2013-11-25T11:59:18Z 2013-11-25T11:59:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
CAMARGO, Sandro da Silva; ENGEL, Paulo Martins. Predicting reservoir quality in sandstones through neural modeling. Vetor, Rio Grande, v. 22, n. 1, p. 57-70, 2012. Disponível em: <http://www.seer.furg.br/vetor/article/view/1337/2140>. Acesso em: nov. 2013. 0102-7352 http://repositorio.furg.br/handle/1/4242 |
identifier_str_mv |
CAMARGO, Sandro da Silva; ENGEL, Paulo Martins. Predicting reservoir quality in sandstones through neural modeling. Vetor, Rio Grande, v. 22, n. 1, p. 57-70, 2012. Disponível em: <http://www.seer.furg.br/vetor/article/view/1337/2140>. Acesso em: nov. 2013. 0102-7352 |
url |
http://repositorio.furg.br/handle/1/4242 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da FURG (RI FURG) instname:Universidade Federal do Rio Grande (FURG) instacron:FURG |
instname_str |
Universidade Federal do Rio Grande (FURG) |
instacron_str |
FURG |
institution |
FURG |
reponame_str |
Repositório Institucional da FURG (RI FURG) |
collection |
Repositório Institucional da FURG (RI FURG) |
repository.name.fl_str_mv |
Repositório Institucional da FURG (RI FURG) - Universidade Federal do Rio Grande (FURG) |
repository.mail.fl_str_mv |
|
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1807384380559065088 |