Reducing geological uncertainty through coupled flow-geomechanics based surrogate models and rejection sampling of CO2 plume prediction

Abstract

Geological CO 2 storage (GCS) is vital in the worldwide pursuit of decarbonization. The scale-up of GCS projects, however, seems to fall short of expectations due to technical and operational difficulties in the deep geological formations. Robust reservoir characterization considering geological uncertainties due to limited data are perhaps the major bottlenecks of GCS large scale deployment. Here we present a workflow to quantify geological uncertainty by the assimilation of CO 2 plume maps acquired from 3D time-lapse seismic surveys. A data-driven proxy model is used to overcome computational constraints; therefore, enables the consideration of large-scale geological uncertainties by considering over 10,000 realizations for uncertainty quantifications. The proposed workflow is implemented for an operating geological CO 2 storage site in the deep saline aquifer of Williston Basin in Canada. The proxy model is not only capable of predicting CO 2 plume evolution with high accuracy, but also shows a notable computational time reduction. A considerable reduction in geological model uncertainty is achieved using the rejection sampling based on matching with assumed seismic interpretation. Among the 10,000 geological realizations, only 1066 realizations are accepted as posterior models with reduced geological uncertainty. The uncertainty quantification method proposed in this study effectively addresses geological model uncertainties based on available seismic survey and provides valuable insights into consideration of the geological uncertainty in CO 2 storage modeling and design of measurement, monitoring and verification (MMV) program for CO 2 storage projects. • Presents a workflow for using 4D seismic maps to quantify uncertainties in carbon storage geological models. • Integrates geomechanics into the simulation of CO 2 plume migration and pressure evolution. • Utilizes Deep learning algorithms to reduce computational burden arising from high resolution numerical simulations and uncertainty quantification workflows. • Presents a methodology for estimating plume size from numerical simulations. • Applies the proposed approach to an existing CO2 demonstration project in Western Canada • Demonstrates the importance selection appropriate CO2 saturation error for quantifying uncertainty in geological models.

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