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

Abstract

We present a case study of oil sands reservoir characterization using resistivity and density seismic volumes estimated from multicomponent seismic data. In the Athabasca Oil Sands region of Alberta, the Lower Cretaceous McMurray Formation is the reservoir. In this reservoir the sand can be differentiated from shale based on density. The sands could be wet or bitumen bearing and resistivity proved to be the only property which can differentiate between the two kinds of sands. We estimated resistivity from multicomponent 3D seismic data integrated with cores and wells data. Our workflow includes petrophysical analysis, joint PP-PS prestack inversion and neural network analysis. Joint PP-PS prestack inversion produces very good estimates of the elastic properties: P-and S-wave velocities and density. Neural network analysis is used for density and resistivity estimation. In all neural network analyses the most significant seismic attributes include converted-wave information. Key problems solved include excellent pay characterization, correct flank water mapping and accurate identification of high versus low oil saturation in zones with high salinity bitumen sand.