Rock Physics And 3c 3d Seismic Analysis For Reservoir Characterization

Download or read book Rock-physics and 3C-3D Seismic Analysis for Reservoir Characterization written by Fabiola Del Valle Ruiz Pelayo and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The elastic properties (density and velocity) of organic shales are largely controlled by kerogen content, porosity, clay content, and e ective pressure. Since surface-seismic measurements can have a complicated dependence on rock properties, it is essential to understand the relationship between the elastic response and variations in rock properties to correctly assess the target reservoir. In this sense, a combination of rock-physics and seismic modeling is applied to relate variations in key properties, such as kerogen content and porosity, to di erences in the elastic response of a 3C-3D seismic volume in the Marcellus Shale (Bradford County, Pennsylvania). Well log analysis and rock physics modeling indicate that density is more sensitive to kerogen content than Vp/Vs or P impedance. Organic-rich intervals (kerogen content > 6 wt. %) are characterized by densities lower than 2.5 g/cc. Vp/Vs and P-impedance are more sensitive to variations in clay content than density; Vp/Vs values lower than 1.6 are attached to clay content lower than 25 %. The interplay between mineralogy and kerogen content causes an increase in velocity in the organic-rich interval, where the e ect of kerogen on the elastic moduli seems to be masked by a decrease in clay content and increase in quartz and calcite. Elastic AVA modeling shows that the sensitivity to the presence of the organic-rich facies increases with angle for both PP and PS (converted-wave) reflections. Additionally, the compressibility seems to be more sensitive to the organic-rich facies than the rigidity. A comparison between PP and PP-PS inversions show that the addition of PS data decreases the P-impedance, S-impedance and density estimation errors by 58, 80, and 17 %, respectively. We used this procedure to create 3D-density maps to indicate promising reservoir quality. These predictions suggest good reservoirs where two gas wells (not used in the analysis) are producing.