aifenaike / logio

Logio is an Unsupervised Machine learning framework for well log visualization, and well-well depth correlation using logs.

Logio

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

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LAS Files

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.

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Features

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 without https)
• 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.

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Dependencies

This project uses Python 3 with dependencies provided in requirements.txt.

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Documentation

See the Tutorials to explore the framework step-by-step in jupyter notebooks and the documentation for more details.

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Installation

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

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Getting Started

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}

Loading

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']) 

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Credits and References

• Schlumberger Log Interpretation Principles\Applications
• lasio
• Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package

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Contributing

Please see CONTRIBUTING.md.

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Support

For support, email [email protected]

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Authors

Please see AUTHORS.md.

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