Speaker
Description
Evaluating CO2 performance in geological carbon storage requires the use of multiple simulation models. The Virtual Learning Environment for Geological Carbon Storage (VIRGO) translates technical outputs from numerical simulations and machine learning (ML) models into actionable insights for developers, operators, regulators, and stakeholders. By incorporating various modules from the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART), VIRGO effectively addresses key questions regarding CO2 storage, such as current CO2 locations, strategies for managing CO2 migration, and evolving Areas of Review (AoR).
With the Rapid Visualization Environment interface, VIRGO empowers users to explore hypothetical scenarios and assess the impacts of varying inputs on reservoir simulations and ML models. Utilizing data from the Illinois Basin – Decatur Project (IBDP), VIRGO enables users to examine changes in pressure and saturation over time, influenced by injection rates and permeability profiles, while integrating information from unified simulation modules and ML models.
As part of CO2 storage studies, VIRGO provides a comprehensive design workflow featuring ML-driven algorithms for the rapid assessment of CO2 plume dynamics, saturation, pressure, and AoR, along with annotated datasets for ML model inference and visualization. Integrated within the SMART framework, VIRGO enhances 3D visualization of extensive grids, which is essential for carbon storage applications.
The application of VIRGO to IBDP datasets demonstrates its accuracy and effectiveness in visualizing well data, pressure, saturation, ML outputs, and AoR analyses. This computer-based learning environment significantly improves field development and monitoring strategies, offering valuable benefits to the carbon capture and storage industry.
Keyword: CO2 Performance, Virtual Learning Environment, Areas of Review (AoR), Data Visualization, Carbon Capture and Storage, Illinois Basin – Decatur Project, Decision Support
| Country | USA |
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