Feasibility of seismic time-lapse monitoring of CO2 with rock physics parametrized full waveform inversion

Abstract

Summary Carbon capture and storage is an important technology for greenhouse gas mitigation. Monitoring of CO2 storage should, in addition to locating the plume, provide quantitative information on CO2 saturation. We propose a full waveform inversion (FWI) algorithm for the prediction of the spatial distribution of CO2 saturation from time-lapse seismic data. The methodology is based on the application of a rock-physics parameterized FWI scheme that allows for direct updating of reservoir properties. We derive porosity and lithology parameters from baseline data and use them as input to predict CO2 saturation from monitor data. The method is tested on synthetic time-lapse data generated for the Johansen formation model. Practical issues associated with field data applications, such as acquisition limitations, construction of the initial model, noise, and uncertainty in the rock physics model, are taken into account in the simulation. The results demonstrate the robustness of our approach for reconstructing baseline and monitor models. We also illustrate the potential of the approach as compared to conventional two-step inversion algorithms, in which an elastic FWI prediction of velocities and density is followed by rock physics inversion.

CO2 Storage Monitoring via Time-Lapse Full Waveform Inversion with Automatic Differentiation

“…Compared to traveltime tomography, TLFWI has the capability to refine high-resolution physical images from time-lapse datasets by fully utilizing the physical information embedded in waveform data [ 14 ]. The fundamental goal of traditional TLFWI is to reduce the discrepancy between the observed data and their predicted counterpart; typically, this objective is attained via optimization techniques that are gradient-based.…”