Effective detection of CO₂ leakage: a comparison of groundwater sampling and pressure monitoring

Journal Article · Wed Dec 31 00:00:00 EST 2014 · Energy Procedia (Online)

DOI:https://doi.org/10.1016/j.egypro.2014.11.448· OSTI ID:1360693

Keating, Elizabeth ^[1]; Dai, Zhenxue ^[1]; Dempsey, David ^[1]; Pawar, Rajesh ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Shallow aquifer monitoring is likely to be a required aspect to any geologic CO₂ sequestration operation. Collecting groundwater samples and analyzing for geochemical parameters such as pH, alkalinity, total dissolved carbon, and trace metals has been suggested by a number of authors as a possible strategy to detect CO₂ leakage. The effectiveness of this approach, however, will depend on the hydrodynamics of the leak-induced CO₂ plume and the spatial distribution of the monitoring wells relative to the origin of the leak. To our knowledge, the expected effectiveness of groundwater sampling to detect CO₂ leakage has not yet been quantitatively assessed. In this study we query hundreds of simulations developed for the National Risk Assessment Project (US DOE) to estimate risks to drinking water resources associated with CO₂ leaks. The ensemble of simulations represent transient, 3-D multi-phase reactive transport of CO₂ and brine leaked from a sequestration reservoir, via a leaky wellbore, into an unconfined aquifer. Key characteristics of the aquifer, including thickness, mean permeability, background hydraulic gradient, and geostatistical measures of aquifer heterogeneity, were all considered uncertain parameters. Complex temporally-varying CO₂ and brine leak rate scenarios were simulated using a heuristic scheme with ten uncertain parameters. The simulations collectively predict the spatial and temporal evolution of CO₂ and brine plumes over 200 years in a shallow aquifer under a wide range of leakage scenarios and aquifer characteristics. Using spatial data from an existing network of shallow drinking water wells in the Edwards Aquifer, TX, as one illustrative example, we calculated the likelihood of leakage detection by groundwater sampling. In this monitoring example, there are 128 wells available for sampling, with a density of about 2.6 wells per square kilometer. If the location of the leak is unknown a priori, a reasonable assumption in many cases, we found that the leak would be detected in at least one of the monitoring wells in less than 10% of the scenarios considered. This is because plume sizes are relatively small, and so the probability of detection decreases rapidly with distance from the leakage point. For example, 400m away from the leakage point there is less than 20% chance of detection. We then compared the effectiveness of groundwater quality sampling to shallow aquifer and/or reservoir pressure monitoring. For the Edwards Aquifer example, pressure monitoring in the same monitoring well network was found to be even less effective that groundwater quality monitoring. This is presumably due to the unconfined conditions and relatively high permeability, so pressure perturbations quickly dissipate. Although specific results may differ from site to site, this type of analysis should be useful to site operators and regulators when selecting leak detection strategies. Given the spatial characteristics of a proposed monitoring well network, probabilities of leakage detection can be rapidly calculated using this methodology. Although conditions such as these may not be favorable for leakage detection in shallow aquifers, leakage detection could be much more successful in the injection reservoir. We demonstrate proof-of-concept for this hypothesis, presenting a simulation where there is measurable pressure change at the injection well due to overpressurization, fault rupture, and consequent leakage up the fault into intermediate and shallow aquifers. The size of the detectible pressure change footprint is much larger in the reservoir than in either of the overlying aquifers. Further exploration of the range of conditions for which this technique would be successful is the topic of current study.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1360693
• Report Number(s): LA-UR-14-27155
• Journal Information: Energy Procedia (Online), Vol. 63; ISSN 1876-6102
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

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54 ENVIRONMENTAL SCIENCES