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:

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
Loading Multiple LAS Files Medium Link
Adding Formation Data to a Log Plot Medium Link
Working with DLIS Files Using DLISIO - Medium Link
How to use Unsupervised Learning to Cluster Well Log Data using Python - Medium
Exploring Well Log Data Using the Welly Python Library - Medium Link
Creating a Core Data Dashboard Using Matplotlib's subplot2grid functionality - Medium Link
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.

License

CoderXiaopang/Petrophysics-Python-Series

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