Hyperspectral data as a proxy for porosity estimation of carbonate rocks

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Kupssinsku, L. S. ; Guimaraes, T. T. ; Cardoso, M. d B. ; Bachi, L. ; Zanotta, D. ; Estilon de Souza, I ; Falcao, A. X. ; Velloso, R. Q. ; Cazarin, C. L. ; Veronez, M. R. ; Gonzaga, L., Jr.

• Tipo de documento: Artigo Científico
• Fonte: AUSTRALIAN JOURNAL OF EARTH SCIENCES; v. 69, n. 6, p. 15-pg., 2022-03-23.

Resumo

Rock porosity is one of the most significant parameters in fluid-flow simulation in the context of carbonate reservoirs. The hydrocarbon industry uses porosity to assess the production potential of oil and gas in carbonate environments. Traditional methods to determine porosity are limited to discrete measurements and generally demand extra resources associated with careful analysis of logs, rock sampling and laboratory analysis. This paper investigates an alternative to estimate porosity in carbonate rocks using pointwise hyperspectral data and machine learning. The method is contiguous, does not require rock sampling and was validated in various rock plug samples collected from two distinct carbonate outcrops. The samples were analysed in the laboratory to determine ground-truth values for the effective porosity and reflectance in visible and infrared regions of the spectra. The supervised regression methods applied were able to estimate a robust relationship between the effective porosity of carbonate rocks and spectral behaviour in characteristic spectral features of carbonate, hydroxyl, molecular water and Fe/Mn. The results obtained here suggest the soundness of the indirect approach to estimate porosity with most of the models trained achieving a coefficient of determination above 0.8 and mean absolute deviation of less than 2%. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático