The effects of pore connectivity on the permeability of coquinas (carbonate rocks) from the Morro do Chaves Formation, Sergipe-Alagoas Basin, Brazil
Ano de defesa: | 2020 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | eng |
Instituição de defesa: |
Universidade Federal do Rio de Janeiro
Brasil Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia Programa de Pós-Graduação em Engenharia Civil UFRJ |
Programa de Pós-Graduação: |
Não Informado pela instituição
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Departamento: |
Não Informado pela instituição
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País: |
Não Informado pela instituição
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Palavras-chave em Português: | |
Link de acesso: | http://hdl.handle.net/11422/23199 |
Resumo: | The heterogeneity of the pore structure of carbonate rocks is manifested by different types, sizes and shapes of pores resulting from sedimentation and diagenetic actions. These complexities increase uncertainties in the estimated hydraulic properties since different permeability values can occur for samples having similar porosities. Investigations were carried out using coquinas taken from a continuous core extracted from the Morro do Chaves Formation, which is considered an analogue of Brazilian presalt carbonate oil reservoirs. The aim was to improve permeability predictions and to analyze the influence of pore connectivity on fluid flow within the pore system. For this purpose, rock types were developed based on routine core analysis. Different petrophysical and statistical techniques were used to confirm similarities between the samples in the clusters. The connectivity study was carried out using microtomographic images of samples having similar porosities but different permeabilities. The 3D images showed that the interconnection of the pore system favored flow within the pores, with the tortuosity and narrowing of the pore throats also impacting the permeability of the pore system. Permeabilities of the coquinas were further analyzed using the Kenyon and Timur-Coates models. Multiple linear regression techniques were used to optimize the various constants in these equations.Também disponível on-line. |