lithology

List of resources for mineral exploration and machine learning, generally with useful code and examples.

Global Scalable Paleo Landscape Evolution Model

We have used the new hierarchical carbonate reservoir benchmarking case study created by Costa Gomes J, Geiger S, Arnold D to be used for reservoir characterization, uncertainty quantification and history matching.

Python package for Exploratory Lithology Analysis

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.

Python package for Petrophysical analysis.

Analysis notebooks for the geolink well log dataset

A probability based approach to characterize lithology using drilling data and Random Forests

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 sandstone…

Handle classification within volcanic formation using supervised learning.

Lithology type classification

Machine learning project for lithology classification from well log data using XGBoost. Predicts Sandstone, Shale, and Limestone from petrophysical logs.

SyleFileCrator for INSPIRE

A mini dataset of lithology microscopic images. This Dataset was developed under supervision of Dr. Keyvan RahimiZadeh and in collabotion with Prof. Amin Beheshti.

Predicting lithology from drillhole geochemistry - honest, spatially-validated ML on open SARIG data (CLR features, GroupKFold by drillhole).

Tools for plotting and analyzing stratigraphic data in R

Calculate each facies proportion for each well in a field and plot them as bubble map distribution

Automated well log interpretation: lithology classification & porosity estimation

A package to extract information from drillholes to feed 3D modelling packages

Facies Classification is one of the process to devine the lithology, with unsupervised learning K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) it will be easily devine type of lithology with automation classification.