BECAS
BLANCO Gonzalo
congresos y reuniones científicas
Título:
Modelos de velocidad para shales orgánicas de Vaca Muerta calibrados con datos de laboratorio y de pozos
Autor/es:
GONZALO BLANCO; CLAUDIA L. RAVAZZOLI; JUAN C. SOLDO
Lugar:
Mendoza
Reunión:
Congreso; 10° Congreso de Exploración y Desarrollo de hidrocarburos; 2018
Institución organizadora:
Instituto Argentino del Petróleo y del Gas (IAPG)
Resumen:
As it is well known, organic rich shales from Vaca Muerta formation are the most important source and unconventional reservoir rocks in Neuquén basin. Understanding the relation between the bulk elastic parameters and wave velocities in these rocks, with parameters such as lithology, clay content, kerogen content, fluid saturation and porosity, is a very important task in rock physics. With this motivation in this paper we present a comparative analysis of models to fit sonic wave velocities in a well located within the oil generation window zone. To accomplish this goal we use log data (compressional and shear sonic logs, effective and total porosity, kerogen fraction and bulk density), and laboratory data (petrophysical, mineralogy, geochemical and fluid characterization). We present two workflows based on the classical Hashin-Shtrikman and Gassmann?s theories, combined with two different empirical elastic models for the dry shale matrix. Taking into account the variability of the physical properties of kerogen and clay minerals, and their influence on the mechanical properties of the shales, the procedure also involves the inversion of such parameters. The results found for the effective elastic properties and density of clay minerals (including bound water) are reasonable taking into account those published in the literature. Regarding the inversion of kerogen physical properties from log data, we found that their estimation is not entirely reliable, so the study should be extended to a greater number of data. As regards to the different velocity models for the sonic velocities, we found very good results, with RMS errors below 4.5%. We remark that this accuracy was possible due to the combination of log and laboratory data and parameter inversion procedure as well.