Python library for reading and writing well data using Log ASCII Standard (LAS) files

Framework for correlating two or more well logs using feature vectors generated from CNN's in Pytorch

Stratigraphic pick prediction via supervised machine-learning

d3.js v5 visualization of well logs

Petroleum Data Collector

Velocity model building by deep learning. Multi-CMP gathers are mapped into velocity logs.

LAS Explorer is a Streamlit web app that allows you to understand the contents of a LAS file. Also includes the ability to identify missing data intervals.

JavaScript for converting well-log standard .las file format to json format

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.

Migrated to: https://gitlab.com/Oslandia/qgis/QGeoloGIS

Typescript/JavaScript library for parsing standard well log files (Geophysical well logs))

stratigraphic machine-learning - active work moved to Predictatops

Open source, public notebooks for working with DLISIO

Well logs correlation using dynamic time warping

Python library for parsing standard well log files (Geophysical well logs)

To identify lithologies, geoscientists use subsurface data such as wireline logs and petrophysical data. However, this process is often tedious, repetitive, and time-consuming. This project aims to use machine learning techniques to predict lithology from petrophysical logs, which are direct indicators of lithology.

Go library for parsing standard well log files (Geophysical well logs)

Upload well LAS (log ASCII) files and view the raw logs interactively in streamlit

Raton Basin Colorado well log data collection and geothermal modeling.

Node.js application for parsing, viewing and make calculations on .las files