lithology

There are 2 repositories under lithology topic.

• RichardScottOZ / mineral-exploration-machine-learning

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

  artificial-intelligencedata-sciencedeep-learningmachine-learningmineral-explorationmineralscoppergeochemistrygeologygeophysicsgeosciencemodellingrocksprospectivitydatalithologynickelspectral-unmixingnlpstratigraphy
  135
• gospl

  Geodels / gospl

  Global Scalable Paleo Landscape Evolution Model

  paleogeographysediment-transportpaleoclimatelandscapelandscape-evolution-modelbasin-modelingsedimentationerosion-processlithologycompaction
  Language:Python 54
• GebPy

  MABeeskow / GebPy

  GebPy is a Python-based, open source tool for the generation of geological data of minerals, rocks and complete lithological sequences. The data can be generated randomly or with respect to user-defined constraints, for example a specific element concentration within minerals and rocks or the order of units within a complete lithological profile.

  geologygeosciencessubsurfacerockphysicsmineralphysicsphysicschemistrylithostratigraphystratigraphygeophysicsgeochemistrypythonpython3lithology
  Language:Jupyter Notebook 22

• csiro-hydrogeology / pyela

  Python package for Exploratory Lithology Analysis

  geologylithology3d-modelsdata-miningfaciesfacies-classification
  Language:Python 21

• RamySaleem / Machine-Predict-Lithologies-Using-Wireline-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.

  catboost-modelclassificationlithologylog-analysismachine-learningwell-logswire-line-logsxboostlithology-predictionlog-interpretationlos-classificationsubsurface-data
  Language:Jupyter Notebook 9

• LukasMosser / geolink_dataset

  Analysis notebooks for the geolink well log dataset

  faciesgeologylithologywirelinepetrophysicsmachine-learningdatasetsdataeda
  Language:Jupyter Notebook 8

• luthfigeo / Litho-Classification

  Handle classification within volcanic formation using supervised learning.

  facies-classificationlabellithologypetrophysicsrandom-forestwell-logs
  Language:Jupyter Notebook 3

• RamySaleem / Reservoir-Quality-in-Producing-Sandstones

  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.

  data-analysisdata-analysis-pythondata-visualisationdatasetdatasetsedalithologypandaspythonwell-logswirelinelithology-logs
  Language:Jupyter Notebook 3

• aifenaike / Probabilistic-Lithology-Characterization

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

  drilling-engineeringlithologyrandom-forestprobabilistic-modelsaucmulti-class-classification
  Language:Jupyter Notebook 2

• imranfadhil / quick_pp

  Python package to assist in providing quick-look or preliminary petrophysical estimation.

  lithologypermeabilitypetrophysicsporositywater-saturation
  Language:Lasso 2

• AhmadTaheri2021 / Lithology-microscopic-images-mini-dataset

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

  classificationdatasetlithologymicroscopic-imageslithology-microscopic-images
  Language:Python 1
• Algemeen

  Died1808 / Algemeen

  Python scripts voor bewerken Nederlandse en Vlaamse bodeminformatie

  gefondergrondvlaanderennederlandsequencestratigraphylithologycpthydrologyhydrologiebodemopbouwgeotechnicsgeotechnieksonderingen
  Language:Python 1

• geolba / StyleFileCreator

  SyleFileCrator for INSPIRE

  arcmap-addininspiremvvm-lightgeologylithologygeologicunitgeologicageesri-stylefileentity-frameworkwizard-steps
  Language:C# 1

• luthfigeo / Facies-Bubble-Map

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

  faciesfacies-proportionlithologyvisualization
  Language:Python 1

• pacifikus / oil-hack

  Lithology type classification

  oil-datalithologyxgboostpycaret
  Language:Jupyter Notebook 1

• warnuk / strattools

  Tools for plotting and analyzing stratigraphic data in R

  geochemistrygeologylithologypaleoclimatestratigraphylithofacies
  Language:R 1

• luthfigeo / Facies-Percentage

  Calculate facies percentage within specific intervals

  facieslithologyspecific-intervalsvisualization
  Language:Jupyter Notebook 0

• RichardScottOZ / dh2loop

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

  drillingfuzzy-matchinggeochemistrygeologylithologymachine-learningnlpdownhole-surveymineral-explorationopen-datareports
  Language:Jupyter Notebook

• RichardScottOZ / geoscience_language_models

  GloVe and BERT language models re-trained using geological text.

  canadadata-sciencegeologylithologymachine-learningmapmineral-explorationnlpreportsmultiprocessingpdf-extractorpipelinedatas-sciencebertgeoscienceglove
  Language:Jupyter Notebook