Petrophysics-Python-Series

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A series of Jupyter notebooks showing how to load well log and petrophysical data in python.

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- - 15_19_F1B_WLC_PETRO_COMPUTED_INPUT_1-checkpoint.las
  - data-checkpoint.md
  - xeek_train_subset-checkpoint.csv
  - Xeek_train_subset_clean-checkpoint.csv
  - Xeek_Well_15-9-15-checkpoint.csv
- - .DS_Store
  - L0506_comp.las
  - L0507_comp.las
  - L0509_comp.las
  - L05B03_comp.las
  - NLOG_LIS_LAS_1722_L0508_19991005-3-01_B1_HRLA_PEX_ACTS_032_MAIN.las
  - NLOG_LIS_LAS_9959_8552_l0509_2002_comp.las
  - WLC_PETRO_COMPUTED_1_INF_1.ASC
- - 15_9_19_SR_Formations.csv
- - NSTA_Offshore_Fields_WGS84.geojson
- - .DS_Store
  - Notebook 22 - VOLVE - 15_9-19.csv
- - 7264_l0701_1971_comp.las
  - 7265_l0704_1973_comp.las
  - 7266_l0705_1974_comp.las
  - L07-01 Stratigraphy Data.csv
  - L07-04 Stratigraphy Data.csv
  - L07-05 Stratigraphy Data.csv
- - wellboreExplorationAll.csv
- - .DS_Store
  - 15-9-19_SR_COMP.las
  - 15_9-F-11A.LAS
  - 15_9-F-11B.LAS
  - 15_9-F-12_Survey_Data.csv
  - 15_9-F-1A.LAS
  - 15_9-F-1B.LAS
  - 15_9-F-1C.LAS
  - 15_9_19_SR_TOPS_NPD.csv
  - volve_wells.csv
- - ExampleData.csv
  - P11-A-02_SURV.csv
  - well_header.json
- - train.zip
- .DS_Store
- .Rhistory
- 15-9-19_SR_COMP.LAS
- 15_19_F1B_WLC_PETRO_COMPUTED_INPUT_1.las
- 15_9-19.csv
- 15_9-19A-CORE.csv
- force2020_data_unsupervised_learning.csv
- L0509_comp.las
- L0509WellData.csv
- micp_sample6.csv
- NLOG_LIS_LAS_7857_FMS_DSI_MAIN_LOG.DLIS
- P11-A-02_Composite_MEM_Image_NF.las
- P11-A-02_SURV.csv
- VolveWells.csv
- xeek_force_2020_dtc_mapping.csv
- xeek_train_subset.csv
- Xeek_train_subset_clean.csv
- Xeek_Well_15-9-15.csv
- - .DS_Store
  - OutputLAS_FINAL.las
- .DS_Store
.DS_Store
.gitignore
01 - Loading and Displaying Well Data From CSV.ipynb
02 - Displaying a Well Plot with MatPlotLib.ipynb
03 - Displaying Histograms and Crossplots.ipynb
04 - Displaying Core Data and Deriving a Poro Perm Relationship.ipynb
05 - Petrophysical Calculations.ipynb
06 - Displaying Formations on Log Plots.ipynb
07 - Working With LASIO.ipynb
08 - Curve Normalisation.ipynb
09 - Visualising Data Coverage - Multi Well.ipynb
10 - Exploratory Data Analysis with Well Log Data.ipynb
11 - Poro-Perm Relationships.ipynb
12 - Enhancing Log Plots With Plot Fills.ipynb
13 - Displaying LWD Image Data.ipynb
14 - Displaying Lithology Data.ipynb
15 - Loading Multiple LAS files to a Dataframe.ipynb
16 - Advanced Well Log Plots - Adding Formation Data to a Well Log Plot.ipynb
17 - Loading DLIS Data.ipynb
18 - Unsupervised Clustering for Lithofacies.ipynb
19 - Getting Started with Welly.ipynb
20 - Core Data Visualisation - Matplotlib subplot2grid.ipynb
21 - Identifying Outliers with Boxplots in Matplotlib.ipynb
22 - CSV File to LAS File.ipynb
23 - Pandas Profiling.ipynb
24 - Creating Poro-Perm Crossplots With Seaborn.ipynb
25 - Semi-Log Crossplot Regression - Poro-Perm.ipynb
26 - Creating Stereonets in Python.ipynb
27 - Random Forest for Lithology Classification - Multi Class Output.ipynb
28 - Seaborn Boxplots of Well Log Data.ipynb
29 - Random Forest for Regression - Prediction of Continuous Well Logs-Copy1.ipynb
30 - Creating A Combined Well Dataframe.ipynb
31 - Creating Well Paths with wellpathpy.ipynb
32 - Creating Geospatial Heatmaps of DT Measurements.ipynb
33 - Auto Outlier Detection - Isolation Forest.ipynb
34 - Folium for Well Mapping.ipynb
35 - ANN for Regression - Prediction of Continuous Well Logs.ipynb
36 - Combining Multiple LAS Files and Formation Data.ipynb
37 - Classification with K-Nearest Neighbors.ipynb
38 - Visualising Lithology Data - Alternatives to Pie Charts.ipynb
39 - dtale for EDA.ipynb
40 - Geojson.ipynb
41 - Creating Waffle Charts of Lithology Data.ipynb
LICENSE
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/andymcdgeo/Petrophysics-Python-Series

Downloads the entire project code from GitHub to your computer.

cd Petrophysics-Python-Series

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

jupyter notebook

Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.

✅ If it runs without errors and prints output in the terminal, it worked.