WHERE ARE THE DATA? AUTOMATING A WORKFLOW FOR CARBON STORAGE DATA GAP ANALYSES

Geologic carbon storage (GCS) is a key decarbonization strategy that can be deployed at a variety of locations and settings. Identifying sites suitable for GCS requires evaluating the intersection of myriad factors, including reservoir conditions, subsurface and surface hazards, infrastructure requirements, and environmental and social justice. The technical viability of a site can only be confirmed for instances where all these factors have data available, and where those data support viability. Yet, many published site selection criteria or workflows focus on geologic subsurface and engineering factors, while partially or completely omitting infrastructure and environmental and social justice aspects.

We present a spatial analysis workflow to assess data availability for the many components of GCS technical viability. The workflow relies upon a knowledge-data framework that links the different components of GCS technical viability to the data types needed for evaluation. Using this contextual information, a combination of data science methods (e.g., natural language processing) and spatial analyses are applied to identify areas where sufficient data exists for a given component. The results are aggregated into maps illustrating data density and spatial gaps across all technical viability factors and data categories, as well as the individual component and category level for a more nuanced understanding. An example use case is provided using public data from NETL’s Carbon Storage Technical Viability Database.