Society of Petroleum Engineers, University of Ibadan Chapter
Submission for the SPE Lagos Section Hackathon
Python package for well log analysis and visualization. Also, this package is an Unsupervised Machine learning framework for well-well depth correlation using logs.
LAS Files
Features
Dependencies
Documentation
Installation
Getting Started
Credits and References
Contributing
Support
Authors
LAS, short for Log ASCII Standard (LAS) files, are generated in borehole operations such as geophysical, geological, or petrophysical logs. file contains
Well log data saved in LAS file generally contains information, including its file version, well description, physical rock curve along with data table and other information related to the well data typically used in well log analysis.
Here are a few things this package does well:
- Loads LAS data from various sources:
- URL link (
https://example.com/.../.../path/to/lasfile.LAS
) - Local file (
path/to/lasfile.LAS
instead withouthttps
)
- URL link (
- Robust IO framework for loading data from flat files (CSV and delimited), Excel files, las files and JSON.
- Parsing well log data into any of the formats mentioned above.
- Hardcoded and flexible implementations for visualization of well logs and non-well log data, but in log format
- A novel system for well-to-well log correlation using dynamic depth warping techniques.
- correlating well logs and obtaining the minimum-cost or "best" match.
This project uses Python 3 with dependencies provided in requirements.txt.
See the Tutorials to explore the framework step-by-step in jupyter notebooks and the documentation for more details.
Clone this repository using this command below on Terminal (Linux or Mac) or WSL (Windows).
git clone https://gitlab.com/aifenaike/Logio.git
cd Logio
Python environment setup is recommended for using this project repository.
You can create the environment variable manually by typing the commands below on Linux or MacOS (and also WSL console).
python -m venv venv
source venv/bin/activate
and for Windows.
python -m venv venv
venv/Scripts/activate
You can now proceed to install required packages by running
pip install -r requirements.txt
graph TB
A(logio)--> B((core))
A --> C((logplot))
A --> D((dynamic_time_warping))
C --> E{PlotWell}
C --> F{LogPLot}
B --> G{Analysis}
D --> H{dtw}
Example Session:
Load and plot a well log from .las
file
# Import the packages
>>> from logio.core import Analysis
>>> from logio.logplot import PlotWell, LogPlot
# Read in your data from a .las file
>>> data = Analysis().read_file(filename="data/15_9-F-11B.LAS")
# Plot a GR log with a cutoff delineating shale from sand volumes
LogPlot(data).cutoff_plot(x="GR", y="DEPTH", x_cutoff=0.45, y_range= (0,0),xscale='linear',labels= ['Sand', 'Shale'],
fig_size = (4.5, 7),colors=['#964B00','#101010'])
- Schlumberger Log Interpretation Principles\Applications
- lasio
- Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package
Please see CONTRIBUTING.md
.
For support, email alexander.ifenaike@gmail.com
Please see AUTHORS.md
.