There are 10 repositories under well-logs topic.
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
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.
Velocity model building by deep learning. Multi-CMP gathers are mapped into velocity logs.
Python reader for Canadian Well Logging Society LAS (Log ASCII Standard) files.
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.
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
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.
Well logs correlation using dynamic time warping
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
This is a program with GUI developed with python, to read well logs and model geomechanical properties.
use lasio to analyze and plot digital well log files
Robust representations of oil wells' intervals via sparse attention mechanism
LAS (Log Ascii Standard v2.0) parser in c++: beta-level-software
LAS (Log Ascii Standard v2.0) web utilities and api in Django web framework : beta-level software
Handle classification within volcanic formation using supervised learning.
This project will explore, analyse and visualise publicly available wells datasets from the United States offshore data centre, the USGS boreholes website - Bureau of Safety and Environmental Enforcement (BSEE) https://www.data.bsee.gov/Main/Default.aspx with a particular focus on the Gulf of Mexico (GOM) wells. This project will study sandstones quality as a reservoir, the production history of the operators on the Gulf of Mexico and a well summary report to highlight any possible problem. The reservoir quality analysis will examine relationships between average values of porosity, permeability, depth, temperature, pressure, thickness, age, and play type for data files from 2009 until 2019.The porosity plotted and shown in a wide range of plots as a function of permeability and burial depth. Also, the median (P50) porosity will be plotted against depth to examine the porosity trend. Moreover, this project will investigate the companies oil and gas production in the gulf of Mexico for the last five years. Lastly, the analysis will include an investigation of well summary reports of five wells. The project will include web scrapping to collect online well summary reports to generate a word cloud. The project results can be useful for specifying realistic distributions of parameters for both exploration risk evaluation and/or reservoir modelling by machine learning algorithms in the next project.
A zero-dependency JavaScript library for reading/parsing canadian well-log files (.Las files)
Useful code for geoscientists in the energy sector. More useful code will be added over time. Special thanks to all those coders out there that helped to pave the way.
Workflows developed for to display and interpret conventional well logs, depositional environments, sequence boundaries, heat flows, clay types, fracture intensities, and operational records
[MATLAB inside] Comparative research well log prediction: Genetic algorithm vs Neural Network
El objetivo de este proyecto es obtener un método tal que la computadora sea capaz de realizar una interpretación de registros geofísicos de manera automática y sin intervención humana alguna dado un set de datos que contenga registros geofísicos.
To check log data availability in a LAS file
Robust representations of oil wells' intervals via sparse attention mechanism