5 papers
Probabilistic Upscaling of Hydrodynamics in Geological Fractures Under Uncertainty
Sarah Perez, Florian Doster, Hannah Menke +2
Flow and transport in fractured geological media are strongly controlled by aperture heterogeneity and uncertainty in subsurface characterisation, yet most upscaling approaches rel…
A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction
Alhasan Abdellatif, Hannah P. Menke, Julien Maes +2
Accurately capturing the complex interaction between CO2 and water in porous media at the pore scale is essential for various geoscience applications, including carbon capture and…
Feature-Modulated UFNO for Improved Prediction of Multiphase Flow in Porous Media
Alhasan Abdellatif, Hannah P. Menke, Florian Doster +2
The UNet-enhanced Fourier Neural Operator (UFNO) extends the Fourier Neural Operator (FNO) by incorporating a parallel UNet pathway, enabling the retention of both high- and low-fr…
A Deep-Learning Iterative Stacked Approach for Prediction of Reactive Dissolution in Porous Media
Marcos Cirne, Hannah Menke, Alhasan Abdellatif +3
Simulating reactive dissolution of solid minerals in porous media has many subsurface applications, including carbon capture and storage (CCS), geothermal systems and oil & gas rec…
When Cubic Law and Darcy Fail: Bayesian Correction of Model Misspecification in Fracture Conductivities
Sarah Perez, Florian Doster, Julien Maes +3
Structural uncertainties and unresolved features in fault zones hinder the assessment of leakage risks in subsurface CO2 storage. Understanding multi-scale uncertainties in fractur…