Geological storage of anthropogenic CO2 plays a pivotal role in Carbon Capture and Storage (CCS) projects. The initial stages of site screening, site selection, and storage capacity estimation are crucial for project commencement. Additionally, in the context of class VI permit applications, accurate assessments of reservoir-scale pressure build-up and CO2 plume dimensions during the injection phase are vital. The Enhanced Analytical Simulation Tool (EASiTool) is a versatile platform designed to support the science-based estimation of CO2 storage capacity for Geological Carbon Storage (GCS). It offers a wide range of powerful features to facilitate efficient and precise CO2 storage simulation and estimation. The latest version, EASiTool version 5.0, introduces substantial updates and advantages through its modern, web-based interface, surpassing its previous versions.
EASiTool comprises two primary modules, each tailored to distinct scenarios and reservoir geometries: User-Given Inputs, and Maximum Storage Capacity. These modules cater to potential project sites with predefined injection scenarios and geometries, or general injection estimates based on reservoir characteristics, such as maximum injection pressure. Both modules generate pressure contour maps and CO2 plume extension maps after the injection phase.
Furthermore, EASiTool now includes Geographic Information System (GIS) maps, probability assessments for the Area of Review (AOR), enhanced sensitivity analysis using Monte Carlo Simulation, and the evaluation of storage efficiency factors as new additions. For boundary conditions, the new version leverages analytical models for closed-, or open-boundary basins, and accounting for natural faults. This tool empowers users to obtain optimized storage capacity estimates and injection scenarios, typically delivering results within seconds. The Net Present Value (NPV) model has also been updated to provide a more realistic financial evaluation. The powerful functionalities offered by EASiTool foster a comprehensive decision-making approach, ensuring that choices are based on robust scientific findings. This, in turn, enables a more effective and successful implementation of carbon storage initiatives.
