This series of Jupyter Notebooks take you through various aspects of working with Python and Petrophysical data. A number of the notebooks are accompanied by either a Blog Post or a Medium article. You can find the full list on my website at: http://andymcdonald.scot/python-and-petrophysics
This series consists of:
- Loading and Displaying Well Data - Medium Link
- Displaying a Well Plot with matplotlib
- Displaying histograms and crossplots
- Displaying core data and deriving a regression
- Petrophysical Calculations
- Displaying Formations on Log Plots
- Working with LASIO
- Curve Normalization - Medium Link
- Visualising Data Coverage - Multi Well - Medium Link
- Exploratory Data Analysis with Well Log Data - Medium Link
- Deriving a Porosity - Permeability Relationship - Medium Link
- Enhancing Log Plots With Plot Fills - Medium Link
- Displaying LWD Image Data - Medium Link
- Displaying Lithology Data on a Well Log Plot Using Python Medium Link
- Prediction of missing data using Machine Learning
- Data QC
- More working with LAS files
- Pickling and Unpickling
- Interactive Petrophysical Plotting
- Working with visualising mutliple wells
- Lithology shading
Data for each workbook can be found with this repo's Data sub folder.
All data has been obtained from publicly accessible data repositories. Details for the origins of each file is presented below.
- 15_9-19.csv
- 15_9-19A-CORE.csv
- 15-9-19_SR_COMP.LAS
- VolveWells.csv
Information on the Volve dataset can be found at: https://www.equinor.com/en/what-we-do/norwegian-continental-shelf-platforms/volve.html
- L0509WellData.csv
- L0509_comp.las
- P11-A-02_Composite_MEM_Image_NF.las
- P11-A-02_SURV.csv
Dutch offshore and onshore well data can be accessed from: https://nlog.nl/en
- xeek_train_subset.csv
FORCE: Machine Predicted Lithology https://xeek.ai/challenges/force-well-logs/overview
If you have any suggestions of what you would like to see, please raise a new issue and I will put something together.