Pinned Repositories
Machine-Learning-Competition-2020
SPWLA PDDA’s 1st Petrophysical Data-Driven Analytics Contest -- Sonic Log Synthesis
Machine-Learning-Competition-2021
micp_to_eptr
The notebook shows the principle behind the minimum incremental pore volume (MIPV) and estimating the effective pore-throat radius (EPTR) as outlined in steps in the paper above.
patchify_buki_mod
Slice and re-combine image data into 2D or 3D subvolumes of specific shape.
permeability_calculator
perm_ calculator is a tool for rapid calculation of permeability from raw and processed MICP data. This will help save time spent on manually obtaining permeability estimates using Dastidar, Winland, Swanson, Wells, and Kamath models See our paper for more details on the respective models: Ishola, O. and Vilcáez, J., Machine learning modeling of permeability in 3D heterogeneous porous media using a novel stochastic pore-scale simulation approach.
pore_microstructural_and_flow_modelling
Stack_3D_Unet
unet_BukiMod is a modification to the U-net architecture for semantic segmentation of images.
star_ccm_flow_simulator
This STAR CCM+ java code takes in two csv files (pore bodies and pore throats), use them to generate pore microstructures, and simulate fluid flow through it. The output of this script is a csv file of flow properties through a set number of iterations.
stochastic_pore_microstructural_generator
This code creates CSV files for pore bodies and pore throats of stochastically generated 3D pore microsturtures. The files are intended to make 3D images/ surface files and run simulations in STAR-CCM+.
xray_to_pore_size_distribution
The notebook demonstrates the workflow for obtaining pore size distribution from binarized micro-CT images. The general principle involves identifying each pore, estimating the volume of each pore, and ultimately determining the radius of a sphere with an equivalent volume of each pore.
PorousMedia's Repositories
PorousMedia/stochastic_pore_microstructural_generator
This code creates CSV files for pore bodies and pore throats of stochastically generated 3D pore microsturtures. The files are intended to make 3D images/ surface files and run simulations in STAR-CCM+.
PorousMedia/star_ccm_flow_simulator
This STAR CCM+ java code takes in two csv files (pore bodies and pore throats), use them to generate pore microstructures, and simulate fluid flow through it. The output of this script is a csv file of flow properties through a set number of iterations.
PorousMedia/micp_to_eptr
The notebook shows the principle behind the minimum incremental pore volume (MIPV) and estimating the effective pore-throat radius (EPTR) as outlined in steps in the paper above.
PorousMedia/permeability_calculator
perm_ calculator is a tool for rapid calculation of permeability from raw and processed MICP data. This will help save time spent on manually obtaining permeability estimates using Dastidar, Winland, Swanson, Wells, and Kamath models See our paper for more details on the respective models: Ishola, O. and Vilcáez, J., Machine learning modeling of permeability in 3D heterogeneous porous media using a novel stochastic pore-scale simulation approach.
PorousMedia/xray_to_pore_size_distribution
The notebook demonstrates the workflow for obtaining pore size distribution from binarized micro-CT images. The general principle involves identifying each pore, estimating the volume of each pore, and ultimately determining the radius of a sphere with an equivalent volume of each pore.
PorousMedia/Machine-Learning-Competition-2020
SPWLA PDDA’s 1st Petrophysical Data-Driven Analytics Contest -- Sonic Log Synthesis
PorousMedia/Machine-Learning-Competition-2021
PorousMedia/patchify_buki_mod
Slice and re-combine image data into 2D or 3D subvolumes of specific shape.
PorousMedia/pore_microstructural_and_flow_modelling
PorousMedia/PorousMedia
Config files for my GitHub profile.
PorousMedia/Stack_3D_Unet
unet_BukiMod is a modification to the U-net architecture for semantic segmentation of images.
PorousMedia/stat453-deep-learning-ss21
STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2021)
PorousMedia/xlearn
Deep learning toolbox for x-ray imaging