Rapid Workflow for Quantitative Interpretation of Shale Mineralogical Composition From Seismic Data

Here we propose a workflow for the quantitative interpretation of clay mineral composition in shales that includes a combination of statistical learning methods and seismic inversion. The workflow is validated on a case study from the Northern Carnarvon Basin, the North West Shelf of Australia, where the thick smectite rich shales seal the source rock. To overcome the issues of rock physics modelling for shales we establish the relationship between mineral composition and elastic properties of shales by applying clustering and regression analysis to available petrophysical and laboratory data. Consequently, Amplitude Variation with Offset/Angle (AVO) inversion is applied to seismic data to estimate elastic properties of shales using linearised Zoeppritz reflectivity equations for anisotropic medium. Finally, the smectite probability maps are obtained by translating the elastic properties into smectite content using the regression relationship established earlier.