Staff Research Highlight - Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site
Hwang, H.-T., Jeen, S.-W., Lee, S.-S., Ha, S.-W., Berg, S. J., Miller, K. L., Sudicky, E. A., & Lee, K.-K. (2024). Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site. In Science of The Total Environment (Vol. 950, p. 175256). Elsevier BV. https://doi.org/10.1016/j.scitotenv.2024.175256
“The determined ranges of parameter uncertainty were subsequently employed in HGSUQ for the LHS operation.”
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We’re happy to highlight this publication co-authored by Aquanty personnel (including Dr. Hyoun-Tae Hwang, Dr. Steve Berg, Dr. Killian Miller and Aquanty co-founder Dr. Ed Sudicky) which focuses on improving monitoring network design for detecting potential CO₂ leaks in hazardous material storage facilities. This research specifically utilizes the Korea CO₂ Storage Environmental Management (K-COSEM) test site to develop methodologies that address the challenges posed by uncertainty in detecting subsurface leakages.
GRAPHICAL ABSTRACT
In this research highlight, researchers explored methods to enhance the monitoring network design for Carbon Capture and Storage (CCS) sites by focusing on improving CO₂ leak detection in complex subsurface environments. This study, centered at the Korea CO₂ Storage Environmental Management (K-COSEM) test site, aimed to advance monitoring techniques critical for ensuring the long-term security of stored CO₂ and protecting groundwater resources in storage areas.
Using a combination of cross-well pumping tests, tracer tests, and simulated CO₂ release experiments, the researchers developed a robust monitoring framework capable of detecting subtle changes in subsurface CO₂ levels. By integrating HydroGeoSphere (HGS) with predictive modelling approaches, including Latin Hypercube Sampling (LHS) for uncertainty analysis, the team was able to simulate potential leak scenarios and evaluate CO₂ migration patterns. HGS was instrumental in this research, enabling researchers to perform detailed spatial and temporal analyses of CO₂ transport across multiple monitoring wells, and providing a basis for optimizing network configurations.
The study’s findings indicate that a carefully planned network of monitoring wells, informed by high-resolution simulations and experimental data, can significantly improve the reliability of early leak detection. By addressing the unique challenges associated with CO₂ migration in complex geological formations, the research emphasizes the importance of precision in monitoring network design to ensure effective CCS operations. These advancements not only contribute to safer CO₂ storage but also offer a framework for monitoring approaches in other subsurface environmental applications where robust detection systems are essential.
Abstract:
Exploring the challenges posed by uncertainties in numerical modelling for hazardous material storage, this study introduces methodologies to improve monitoring networks for detecting subsurface leakages. The proposed approaches were applied to the Korea CO2 Storage Environmental Management (K-COSEM) test site, undergoing calibration, validation and uncertainty analysis through hydraulic and controlled-CO2 release tests. The calibration phase involved inter-well tracer and multi-well pumping tests, leveraging the Parameter ESTimation (PEST) model to determine the aquifer flow and solute transport properties of the K-COSEM site. To tackle uncertainties with limited observation data, we adopted Latin Hypercube simulation. Our uncertainty analysis confirmed model accuracy in simulating observed CO2 breakthrough curves. We also explored a probabilistic method to identify the environmental change point (EnCP) through correlation analysis with the distance from the CO2 injection well, revealing a linear trend and pinpointed potential preferential flow pathways by assessing detection probabilities. Evaluating CO2 detection capabilities was crucial for optimizing monitoring well placement, highlighting strategic well selection based on detection probabilities. This study advances managing uncertainties in hydrogeological modelling, underscoring the importance of sophisticated models in designing monitoring networks for hazardous leak detection in complex subsurface conditions.