abv-hub,github

srbdrp_toolbox

Stanford Rock Physics & Borehole Geophysics (SRB) Digital Rock Physics (DRP) Toolbox. The codes include image creation, alteration, and computation of physical and geometrical properties.

standards

Standards for ML models and dataset annotations

stanford-vi-e

Data for the synthetic reservoir Stanford VI including EM modeling

statistical_missteps

Supplement to Three common statistical missteps we make in reservoir characterization

step-detect

Python algorithms for autonomous step detection in 1D data

strata

Markov chain simulator in a sequence stratigraphic framework

stratal-geometries

This repository uses a classifier to predict basin-scale stratigraphic geometries

striplog

Lithology and stratigraphic logs for wells or outcrop.

subsurfacemachinelearning

Short course on subsurface data analytics and machine learning.

Working on lithology identification from labeled log data using a mix of domain knowledge and ML techniques (Clustering, Feature Generation, Outlier Detection, regression, NN and etc). After lithology identification, a model is trained on input conventional log to predict DTC and DTS logs. Learning from both models will be implemented on new unseen data.

synthetic_well-log_polynomial_regression

This project attempts to construct a missing well log from other available well logs, more specifically an NMR well log from the measured Gamma Ray (GR), Caliper, Resistivity logs and the interpreted porosity from a well.

t20-dash_well_viz

The aim is to try and build some interactive log visualisations using dash in notebooks and demonstrate what is possible (and also what the limitations are) using dash on subsurface data. Ultimately it would be good to try and collate all the examples into a library of templates to share after the Hackathon :swung: I have put together a quick Google Sheet with some initial ideas, so feel free to either pick one that interests you or add something to the list that you think would be fun to make, all ideas welcome.

tcn

The repository has the PyTorch codes to reproduce the results for our recently accepted paper, "Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network", in SEG Technical Program Expanded Abstracts, 2019.