Hyperspectral data as a proxy for porosity estimation of carbonate rocks

• Published in: Australian journal of earth sciences Vol. 69; no. 6; pp. 861 - 875
• Main Authors: Kupssinskü, L. S., Guimarães, T. T., Cardoso, M. d. B., Bachi, L., Zanotta, D., Estilon de Souza, I., Falcão, A. X., Velloso, R. Q., Cazarin, C. L., Veronez, M. R., Gonzaga, L.
• Format: Journal Article
• Language: English
• Published: Hoboken Taylor & Francis 18.08.2022; Wiley Subscription Services, Inc
• Subjects: carbonate; Carbonate rocks; Carbonates; Effective porosity; Flow simulation; Fluid dynamics; Fluid flow; Hydrocarbons; Infrared spectra; Laboratories; Machine learning; Manganese; Methods; outcrop; Outcrops; Porosity; Reflectance; Regression; reservoir analogue; Robustness (mathematics); rock porosity; Sampling; Sediment sampling; Support vector machines
• ISSN: 0812-0099; 1440-0952
• DOI: 10.1080/08120099.2022.2046636

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%. KEY POINTS Hyperspectral data can be used as proxy for porosity estimation in carbonate rocks. All the tested learners achieved R 2 greater than 0.7. Regularised linear regression can be used to estimate porosity. Support vector regression estimation of porosity achieves a mean absolute error of 1.0249 in our dataset.