This study uses a sufficient number of core plugs with the objective to combine mercury injection pressure data for pore characterization with conceptional pore type models using sonic velocity. The plugs are analyzed to establish relationships between porosity, pore structure, permeability and log-derived sonic velocity. This paper combines two well-established methods for reservoir quality prediction: pore typing using sonic velocity and pore throat characterization using mercury injection capillary pressure (MICP) data. Microporosity is known to be a significant parameter for permeability prediction. In our dataset microporosity relates to velocity at a given porosity. MICP derived pore throat parameters are linked to microporosity and to sonic velocity. Linking MICP derived petrophysical rock types through microporosity with poroelastic properties opens the potential to predict reservoir quality using sonic data at the log scale. Classifying MICP data by means of fraction of microporosity provides a geologically meaningful classification and interpretation of rock properties.
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Baechle et al. (2024) studied this question.
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