The world is in urgent need of Carbon Capture Storage (CCS) sites/facilities to achieve ambitious net carbon dioxide (CO2) emissions goals. According to the IEA report 2020, more than 1.3 Gt of CO2 would need to be captured from fossils fuel and processes representing an increase of more than 3,000% from todays 0.039Gt! Thus, storage of CO2 in significant quantities requires the efficient identification of large-scale subsurface carbon storage sites. Part of achieving efficiency will be adapting, developing and implementing workflows that can maximize the benefits of digitalization (AI and ML approaches) to reduce turnaround times, while maximizing the amount of information extracted from geoscience data. This paper demonstrates the key steps of an integrated geoscience workflow aiming to liberate geological insights from regional datasets. This workflow has been developed and implemented over a proof- of-concept (PoC) area to demonstrate the rapid assessment of risks associated with capacity and containment, and its quantification. This has been more recently deployed over a larger area comprising of more than 6,000sqkm of seismic data. The workflow is designed to estimate the reservoir properties and geological features such as faults interact spatially to provide subsurface storage, at scale and safely. Reservoir properties derived from wells and seismic allow the 3D distribution of such properties and features to be fully considered. This integrated reservoir geoscience CCS site assessment workflow has been designed to be as automated as possible, creating the possibility of applying it to regional datasets and across multiple sites in the most efficient manner.

Open-Access Online Publication: May 29, 2023