Reducing uncertainty in geologic CO2 sequestration risk assessment by assimilating monitoring data

Abstract

Geologic CO2 sequestration sites usually have large uncertainty in geological properties, such as uncertainty in permeability and porosity fields. Geological uncertainty leads to significant uncertainty in predicted risk metrics such as CO2 plume extent, CO2/brine leakage rates through wellbores, and impacts to drinking water quality in groundwater aquifers due to CO2/brine leakage, all of which will impact the approach for post injection site care (PISC). Pre-injection risk assessment can be used to quantify the amount of uncertainty in different predicted risk metrics. However, it cannot account for the potential value of monitoring data (e.g., CO2 saturation and pressure measurements) acquired during the operation of CO2 storage. In this study, we demonstrate how uncertainty in predicted risks can be reduced by performing monitoring data assimilation. An ensemble of geological reservoir models, constrained by direct measurements (such as permeability estimates from exploratory wells), are generated by geostatistical conditional simulation. As the monitoring data from the storage site become available, they are assimilated into models using a recently developed data assimilation method, ES-MDA with geometric inflation factors (ES-MDA-GEO). The reservoir models, calibrated through multiple data assimilation iterations, are used to predict future risks and reduction in their uncertainties. The proposed approach for the quantification of uncertainty reduction in risk assessment is demonstrated with two examples: a generalized 3D synthetic case and a synthetic field case based on the Rock Springs Uplift site in southwestern Wyoming.