Uncertainty Quantification Through the Assimilation of CO2 Plume Size from 4D Seismic Survey

Abstract

Abstract Geological models of saline aquifers used for CO2 storage inherently involve uncertainties due to limited data. This requires innovative approaches to quantify the impact of geological uncertainties on CO2 plume size and monitoring strategies. To address this issue, data assimilation and history matching have been widely employed. These approaches use diverse measurement, monitoring and verification (MMV) data such as pressure measurements, saturation logs, and surface monitoring data to reduce uncertainties associated with simulations. However, in carbon storage, 4D time-lapse seismic images are crucial and can provide valuable input for assessing uncertainties in CO2 storage models by providing estimates of CO2 plume migration at certain time intervals. In this study, a methodology is proposed to quantify the uncertainties in geological models for CO2 storage by the assimilation of CO2 plume size data derived from 4D seismic images taken at different injection periods. To consider a wide range of uncertainties, data-driven proxy models are developed using high-fidelity coupled reservoir-geomechanics simulations data to overcome the prohibitive computational issues on numerous realizations (>1000). The trained proxy models are used to forecast the CO2 plume size at multiple time intervals for a large sample of newly generated geomodels. A sample rejection procedure is implemented to quantify uncertainty and filter consistent, or history-matched geological realizations. The proposed workflow is implemented for an existing geological CO2 storage site in Western Canada. The proxy model is not only capable of predicting CO2 plume evolution with high accuracy but also shows a notable computational time reduction. A considerable reduction in geological model uncertainty is achieved using the proposed methodology. Among the 10,000 geological realizations, only 926 realizations are accepted as posterior models. 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 CO2 storage modeling and design of MMV program for CO2 storage projects.