Optimizing large-scale CO₂ pipeline networks using a geospatial splitting approach.

Area of Science:

• Environmental Science and Engineering
• Geospatial Analysis
• Carbon Capture and Storage (CCS)

Background:

• Carbon capture and storage (CCS) infrastructure is crucial for mitigating carbon emissions.
• Conventional CO2 pipeline network design often assumes storage sites at basin centroids, potentially leading to suboptimal, lengthy transportation routes.
• The spatial extent of geological storage formations can significantly impact overall pipeline network length and project economics.

Purpose of the Study:

• To develop and present a novel geospatial splitting framework for partitioning large CO2 storage basins into multiple sub-sinks.
• To optimize CO2 pipeline network routes and reduce transportation distances and associated costs.
• To improve the realism and efficiency of CCS project deployment modeling and decision-making.

Main Methods:

• Utilized reservoir models with varying petrophysical properties and injection rates to simulate pressure plume behavior.
• Calculated the optimal number of subregions per basin based on pressure interference and boundary extents.
• Applied K-means clustering and Voronoi polygon algorithms to partition basins and determine sink coordinates.

Main Results:

• The geospatial splitting approach reduced total pipeline network length by 13% (regional) and 10% (nationwide) in case studies.
• Cost reductions of 25% (regional) and 17% (nationwide) were achieved compared to conventional basin centroid modeling.
• Demonstrated that optimal sink location critically impacts CO2 transportation pipeline length and expenses.

Conclusions:

• The developed geospatial framework effectively partitions large basins into optimized sub-sinks for CO2 storage.
• This approach significantly shortens pipeline networks and reduces costs, enhancing CCS project viability.
• The workflow provides a more realistic basis for CCS modeling, supporting informed decision-making for deployment.