Petrophysics-Python-Series

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

Check out the binder button below if you want to run these notebooks without needing to download them or install Python.

Binder button will be added to all new new notebooks going forward

For citation please use: McDonald, A., 2021, Python and Petrophysics Notebook Series. https://github.com/andymcdgeo/Petrophysics-Python-Series

Series Contents:

1. Loading and Displaying Well Data - Medium Link
2. Displaying a Well Plot with matplotlib
3. Displaying histograms and crossplots
4. Displaying core data and deriving a regression
5. Petrophysical Calculations
6. Displaying Formations on Log Plots
7. Working with LASIO
8. Curve Normalization - Medium Link
9. Visualising Data Coverage - Multi Well - Medium Link
10. Exploratory Data Analysis with Well Log Data - Medium Link
11. Deriving a Porosity - Permeability Relationship - Medium Link
12. Enhancing Log Plots With Plot Fills - Medium Link
13. Displaying LWD Image Data - Medium Link
14. Displaying Lithology Data on a Well Log Plot Using Python Medium Link
15. Loading Multiple LAS Files Medium Link
16. Adding Formation Data to a Log Plot Medium Link
17. Working with DLIS Files Using DLISIO - Medium Link
18. How to use Unsupervised Learning to Cluster Well Log Data using Python - Medium
19. Exploring Well Log Data Using the Welly Python Library - Medium Link
20. Creating a Core Data Dashboard Using Matplotlib's subplot2grid functionality - Medium Link
21. Identifying Outliers in Well Log Data Using Boxplots in Matplotlib - Medium Link

Still to come

• Prediction of missing data using Machine Learning
• Data QC
• More working with LAS files
• Pickling and Unpickling
• Interactive Petrophysical Plotting
• Visualising mutliple wells

Data Sets Used

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.

Equinor Volve Dataset

• 15_9-19.csv
• 15_9-19A-CORE.csv
• 15-9-19_SR_COMP.LAS
• 15_19_F1B_WLC_PETRO_COMPUTED_INPUT_1
• 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

NLOG - Netherlands Well Log and Data Repository

• L0509WellData.csv
• L0509_comp.las
• P11-A-02_Composite_MEM_Image_NF.las
• P11-A-02_SURV.csv
• NLOG_LIS_LAS_7857_FMS_DSI_MAIN_LOG.DLIS

Dutch offshore and onshore well data can be accessed from: https://nlog.nl/en

Force 2020 XEEK

• xeek_train_subset.csv

Data was provided by the FORCE Machine Learning competition with well logs and seismic 2020”
Bormann P., Aursand P., Dilib F., Dischington P., Manral S. 2020. 2020 FORCE Machine Learning Contest. https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition

FORCE: Machine Predicted Lithology https://xeek.ai/challenges/force-well-logs/overview

Suggestions

If you have any suggestions of what you would like to see, please raise a new issue and I will put something together.