Petrophysics-Python-Series

⚠️ During 2023 this repo is planned to be restructured and tidied up. A number of notebooks are currently lacking commentary and will be updated gradually.

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.

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

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

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.

Name		Name	Last commit message	Last commit date
Latest commit History 57 Commits
Data		Data
Output		Output
.DS_Store		.DS_Store
.gitignore		.gitignore
01 - Loading and Displaying Well Data From CSV.ipynb		01 - Loading and Displaying Well Data From CSV.ipynb
02 - Displaying a Well Plot with MatPlotLib.ipynb		02 - Displaying a Well Plot with MatPlotLib.ipynb
03 - Displaying Histograms and Crossplots.ipynb		03 - Displaying Histograms and Crossplots.ipynb
04 - Displaying Core Data and Deriving a Poro Perm Relationship.ipynb		04 - Displaying Core Data and Deriving a Poro Perm Relationship.ipynb
05 - Petrophysical Calculations.ipynb		05 - Petrophysical Calculations.ipynb
06 - Displaying Formations on Log Plots.ipynb		06 - Displaying Formations on Log Plots.ipynb
07 - Working With LASIO.ipynb		07 - Working With LASIO.ipynb
08 - Curve Normalisation.ipynb		08 - Curve Normalisation.ipynb
09 - Visualising Data Coverage - Multi Well.ipynb		09 - Visualising Data Coverage - Multi Well.ipynb
10 - Exploratory Data Analysis with Well Log Data.ipynb		10 - Exploratory Data Analysis with Well Log Data.ipynb
11 - Poro-Perm Relationships.ipynb		11 - Poro-Perm Relationships.ipynb
12 - Enhancing Log Plots With Plot Fills.ipynb		12 - Enhancing Log Plots With Plot Fills.ipynb
13 - Displaying LWD Image Data.ipynb		13 - Displaying LWD Image Data.ipynb
14 - Displaying Lithology Data.ipynb		14 - Displaying Lithology Data.ipynb
15 - Loading Multiple LAS files to a Dataframe.ipynb		15 - Loading Multiple LAS files to a Dataframe.ipynb
16 - Advanced Well Log Plots - Adding Formation Data to a Well Log Plot.ipynb		16 - Advanced Well Log Plots - Adding Formation Data to a Well Log Plot.ipynb
17 - Loading DLIS Data.ipynb		17 - Loading DLIS Data.ipynb
18 - Unsupervised Clustering for Lithofacies.ipynb		18 - Unsupervised Clustering for Lithofacies.ipynb
19 - Getting Started with Welly.ipynb		19 - Getting Started with Welly.ipynb
20 - Core Data Visualisation - Matplotlib subplot2grid.ipynb		20 - Core Data Visualisation - Matplotlib subplot2grid.ipynb
21 - Identifying Outliers with Boxplots in Matplotlib.ipynb		21 - Identifying Outliers with Boxplots in Matplotlib.ipynb
22 - CSV File to LAS File.ipynb		22 - CSV File to LAS File.ipynb
23 - Pandas Profiling.ipynb		23 - Pandas Profiling.ipynb
24 - Creating Poro-Perm Crossplots With Seaborn.ipynb		24 - Creating Poro-Perm Crossplots With Seaborn.ipynb
25 - Semi-Log Crossplot Regression - Poro-Perm.ipynb		25 - Semi-Log Crossplot Regression - Poro-Perm.ipynb
26 - Creating Stereonets in Python.ipynb		26 - Creating Stereonets in Python.ipynb
27 - Random Forest for Lithology Classification - Multi Class Output.ipynb		27 - Random Forest for Lithology Classification - Multi Class Output.ipynb
28 - Seaborn Boxplots of Well Log Data.ipynb		28 - Seaborn Boxplots of Well Log Data.ipynb
29 - Random Forest for Regression - Prediction of Continuous Well Logs-Copy1.ipynb		29 - Random Forest for Regression - Prediction of Continuous Well Logs-Copy1.ipynb
30 - Creating A Combined Well Dataframe.ipynb		30 - Creating A Combined Well Dataframe.ipynb
31 - Creating Well Paths with wellpathpy.ipynb		31 - Creating Well Paths with wellpathpy.ipynb
32 - Creating Geospatial Heatmaps of DT Measurements.ipynb		32 - Creating Geospatial Heatmaps of DT Measurements.ipynb
33 - Auto Outlier Detection - Isolation Forest.ipynb		33 - Auto Outlier Detection - Isolation Forest.ipynb
34 - Folium for Well Mapping.ipynb		34 - Folium for Well Mapping.ipynb
35 - ANN for Regression - Prediction of Continuous Well Logs.ipynb		35 - ANN for Regression - Prediction of Continuous Well Logs.ipynb
36 - Combining Multiple LAS Files and Formation Data.ipynb		36 - Combining Multiple LAS Files and Formation Data.ipynb
37 - Classification with K-Nearest Neighbors.ipynb		37 - Classification with K-Nearest Neighbors.ipynb
38 - Visualising Lithology Data - Alternatives to Pie Charts.ipynb		38 - Visualising Lithology Data - Alternatives to Pie Charts.ipynb
39 - dtale for EDA.ipynb		39 - dtale for EDA.ipynb
40 - Geojson.ipynb		40 - Geojson.ipynb
41 - Creating Waffle Charts of Lithology Data.ipynb		41 - Creating Waffle Charts of Lithology Data.ipynb
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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Petrophysics-Python-Series

Series Contents:

Data Sets Used

Equinor Volve Dataset

NLOG - Netherlands Well Log and Data Repository

Force 2020 XEEK

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