5 papers
Property-Constrained 3D Porous Media Reconstruction from 2D Images via Conditional Generative Adversarial Networks
Ali Sadeghkhani, Brandon Bennett, Arash Rabbani
This study presents a conditional Generative Adversarial Network (cGAN) framework for generating 3D porous media volumes with controlled porosity, trained exclusively on 2D thin se…
PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks
Ali Sadeghkhani, Brandon Bennett, Masoud Babaei +1
Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization, as natural spatial heterogenei…
Adaptive Bayesian Structure Learning of DAGs With Non-conjugate Prior
S. Nazari, M. Arashi, A. Sadeghkhani
Directed Acyclic Graphs (DAGs) are solid structures used to describe and infer the dependencies among variables in multivariate scenarios. Having a thorough comprehension of the ac…
A Decade of Generative Adversarial Networks for Porous Material Reconstruction
Ali Sadeghkhani, Brandon Bennett, Masoud Babaei +1
Digital reconstruction of porous materials has become increasingly critical for applications ranging from geological reservoir characterization to tissue engineering and electroche…
Well Log-Guided Synthesis of Subsurface Images from Sparse Petrography Data Using cGANs
Ali Sadeghkhani, A. Assadi, B. Bennett +1
Pore-scale imaging of subsurface formations is costly and limited to discrete depths, creating significant gaps in reservoir characterization. To address this, we present a conditi…