Dataset for the publication "Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage"

DOI:10.4121/a8ad7808-b923-4335-ba7a-898c8c1232be.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/a8ad7808-b923-4335-ba7a-898c8c1232be

Datacite citation style

Serrao Seabra, Gabriel; Vossepoel, Femke; Denis Voskov; Mücke, Nikolaj; Silva, Vinicius et. al. (2025): Dataset for the publication "Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/a8ad7808-b923-4335-ba7a-898c8c1232be.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains 4,847 NetCDF files generated with the Delft Advanced Research Terra Simulator (DARTS). Each file represents a distinct high-resolution reservoir simulation, designed for machine learning research in carbon storage and reservoir engineering. The simulations include pressure, temperature, saturations, flow fields, permeability, porosity, and production variables. This is the dataset used on the publication Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage, which is still preprint.

History

  • 2025-10-13 first online, published, posted

Publisher

4TU.ResearchData

Format

NetCDF4 (.nc)

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Engineering

DATA

Files (4848)