A geochemically informed leak detection (GILD) model for CO2 injection sites

Abstract

Early detection of CO2 leakage through monitoring is important to ensure long-term safety for geologic carbon storage (GCS). A geochemically informed leak detection (GILD) model has been developed for groundwater chemistry monitoring at CO2 injection sites. The GILD model integrates a geochemical model that simulates fluid chemistry changes in CO2 leakage events and a Bayesian belief network (BBN) model that evaluates monitoring observations to identify leakages. The geochemical model is implemented using Geochemists’ Workbench to assess fluid chemistry changes as a result of small CO2 leakage in an above-zone monitoring interval (AZMI) formation with varying mineral assemblages and background fluids. Response functions are fitted to the output of the geochemical model and are translated to conditional probabilities in the BBN model. The BBN model gives operational prediction of the leak probability given a set of groundwater monitoring measurements and the probability of detecting a leak at a given magnitude. The detection capabilities of multiple monitoring parameters are compared. For aquifers that contain calcite, it is valuable to incorporate other monitoring parameters with pH to increase the sensitivity of detection. For aquifers with no calcite, pH alone is a sensitive parameter. This research illustrates a method of identifying CO2 leakage into aquifers with both geochemical and statistical tools.