Data underlying the publication: AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage

DOI:10.4121/fd03d033-1d73-4b9a-91b4-b47e9a102a04.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/fd03d033-1d73-4b9a-91b4-b47e9a102a04

Datacite citation style

Serrao Seabra, Gabriel; Mücke, Nikolaj; Denis Voskov; Silva, Vinicius; Vossepoel, Femke (2025): Data underlying the publication: AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/fd03d033-1d73-4b9a-91b4-b47e9a102a04.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains 1,219 NetCDF files generated with the Delft Advanced Research Terra Simulator (DARTS) using geological models produced with AlluvsIm. Each file represents a distinct realization of channelized reservoirs and includes pressure, saturation, porosity, permeability, and component flow fields. The dataset supports research on geological carbon storage, enhanced oil recovery, and uncertainty quantification, and was created for the study "AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage" (IJGGC, 2024).

History

  • 2025-09-10 first online, published, posted

Publisher

4TU.ResearchData

Format

NetCDF, *.nc

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Engineering
Petroleo Brasileiro S.A. (Petrobras)
Centrum Wiskunde & Informatica
University Utrecht, Mathematical Institute
Imperial College London, Department of Earth Science and Engineering
Stanford University, Department of Energy Resources Engineering

DATA

Files (1222)