Ensuring CO2 Storage and Groundwater Safety through Geochemical Monitoring at CO2 Injection Sites

Geologic carbon storage (GCS) is the injection and storage of carbon dioxide (CO₂), a greenhouse gas, into deep formations following its removal from industrial sources. This process aims to reduce ambient atmospheric CO₂ concentrations and is included as a tool in global climate mitigation strategies. CO₂ enhanced oil recovery (EOR) provides a pathway for economic reuse and storage of CO₂. Safe, long-term storage of CO₂ is important for achieving climate mitigation objectives and to limit risks for human health and the environment, including risks to overlying groundwater aquifers. The main objective of the research was to develop tools for ensuring CO₂ storage and groundwater protection through geochemical monitoring. 

This was accomplished by (1) analyzing produced water samples at a CO₂ injection site, (2) developing a geochemically informed leak detection (GILD) model for groundwater chemistry monitoring at CO₂ injection sites, and (3) illustrating its application to a site on the Gulf Coast with considerations of measurement variability. Statistical analyses of produced water samples were conducted using a two-way analysis of variance (ANOVA) and prediction intervals. ANOVA was aimed to analyze the differences of samples of pre- and post-CO₂ injection, while prediction intervals was aimed to analyze individual wells. 

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 was 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 were fitted to the output of the geochemical model and were translated to conditional probabilities in the BBN model. 

The application of the GILD model requires characterizing the measurement variability from the background data using the coefficient of variations. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions formed 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO₂ leakage concentration. The aggregate model-measurement errors for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. BBN models were constructed with additions of modelmeasurement errors of different levels.

This research assessed the detection probabilities of monitoring systems considering individual monitoring parameter, combinations of monitoring parameters, with spatial and temporal variability. Based on that, this research will help facilitate decision making of early detection of potential CO₂ leakages.