Propagating seismic data quality into rock physics analysis and reservoir property estimation: Case study of lithology prediction using full waveform inversion in clastic basins

Abstract

Rock physics analysis can provide the relation between the parameters (or seismic attributes) that govern seismic wave propagation (e.g., Vp, Vs, and density in isotropic media) and the reservoir property of interest such as lithology, porosity, and saturation. In this process, we need to account for the quality of the seismic data and derive the appropriate uncertainties associated with the seismic data, such as noise, resolution, and inversion artifacts into the reservoir property estimation. In this case study, we show how to quantitatively propagate seismic data quality issues such as resolution, noise, and inversion accuracy into the lithology estimation in a clastic basin. The use of full waveform inversion and Bayesian classification techniques provides a mathematical framework that enable us to model and directly relate data quality input into the uncertainty associated with lithology prediction.