Resistivity and density estimation from multicomponent seismic data: Case study from the Lower Cretaceous McMurray Formation, Athabasca Oil Sands

Abstract

Summary The Lower Cretaceous McMurray Formation reservoir in this study is located in the Athabasca basin of Alberta, Canada. High resolution multicomponent 3D seismic data along with core and well data have been processed using the most advanced workflows in order to image the reservoir heterogeneity. Workflows include petrophysical analysis, joint PP-PS inversion and neural network analysis. Three inversions using PP and PS seismic are analyzed and compared. The joint PP-PS inversion of the prestack seismic data produces the best estimates of P-impedance, S-impedance and density, allowing for excellent reservoir characterization. Neural network analysis is used to enhance the resolution of the elastic properties estimated from joint PP-PS prestack inversion, and to estimate petrophysical and engineering properties such as porosity, resistivity and oil saturation. In all neural network analyses the most significant seismic attributes include converted-wave information. Some of the results are: 1. Converted-wave seismic data have a major role in oil-sands reservoir characterization; 2. Estimated density volume distinguishes sands from nonreservoir facies; 3. Estimated resistivity allows differentiation of bitumen sands from water sands.