Subsurface carbon storage (SCS) is the process of capturing carbon dioxide (CO2) from large stationary sources (e.g., coal-fired power plants and natural gas processing facilities) and storing it in the subsurface for extended time periods. As depicted below, CO2 is piped from its source to a storage reservoir thousands of feet underground. The CO2 is stored in a deep saline aquifer under pressure below an impermeable cap rock (aquiclude), and the impermeable formation is monitored for leaks. Over the long-term, the CO2 is mineralized to a solid, thus removing it from the atmosphere over geologic timescales. SCS is being considered as a climate change mitigation option for near- and long-term scenarios.
Managing Uncertainty in CO2Leakage Risk at Storage Sites
For SCS to be an effective carbon-emission reduction strategy, the injected CO2 must remain isolated from the atmosphere for thousands of years. Further, the injected CO2 cannot be allowed to leak, often through abandoned wells, and degrade water supplies. Finally, injection rates must be controlled to prevent widespread pressure increases in the subsurface that could create fractures in the cap rock and increase the risk of induced seismicity and CO2 leakage. All of these conditions must be met to prevent significant economic impacts to any SCS project.
To evaluate the feasibility and efficacy of SCS projects, complex multiphase modeling efforts are required to evaluate the fate, transport, and geochemistry of injected CO2. This modeling effort includes compiling data generated by a suite of monitoring technologies as shown below.
HGL’s Specialized Experience
HGL leverages a 35-year history of subsurface modeling to predict multiphase flow dynamics using optimized parameters for porosity, permeability, and geochemistry. In addition, HGL has an in-house subject matter expert (SME) with the specialized capability to use the CompFlow code to simulate the complex process of injecting and storing CO2 in deep saline aquifers. HGL uses machine learning and artificial intelligence algorithms along with computational optimization and stochastic analysis to support the design of the optimal long-term monitoring (LTM) networks required to minimize the uncertainty regarding CO2-leakage risk at potential SCS sites. HGL utilizes a three-step approach for designing these LTM networks.
Step 1: Data generated by DOE-funded U.S. Regional Carbon Sequestration Partnership field projects (e.g., EDX database [NETL, 2021]) is used to create machine-learned proxy models (PMs). The PMs can incorporate three types of information: data collected at existing SCS sites using various monitoring; data that could facilitate the use of known characteristics or features of potential SCS sites in conjunction with a numerical CompFlow model; and data collected during the characterization of existing SCS sites that could assist conceptual site model development for potential SCS sites.
Step 2: Data for the potential SCS site; the numerical CompFlow model of the potential SCS site; and the PMs generated in Step 1 for site characterization and for CSM enhancement are combined to generate an enhanced machine-learned PM for the potential SCS site (CompFlow – HGL-Site-PM). This PM will be equally or more accurate and computationally much less expensive to run than the numerical CompFlow model.
Step 3: HGL’s PlumeSeekerTM technology is used in conjunction with the PM of monitoring data generated in Step 1 and the machine-learned PM of the potential SCS site to design an optimal (least-cost) LTM plan. The LTM plan will identify the specific monitoring technology to use to collect the necessary data (e.g., pressure, temperature, CO2-saturation) to ensure that uncertainty regarding CO2-leakage risk is minimized.
Understanding leakage risk is essential for making informed evaluations of potential SCS sites, and HGL offers clients the tools and expertise to identify sites where CO2 can be stored safely and efficiently.
NETL. Best Practices Manual (BMP): Monitoring, Verification, and Accounting (MVA) for Geologic Storage Projects. Revised Edition. DOE/NETL-2017/1847. 2017.
National Energy Technology Laboratory (NETL) Energy Data eXchange (EDX). https://edx.netl.doe.gov/ 2021.