RamySaleem/Machine-Predict-Lithologies-Using-Wireline-logs

To identify lithologies, geoscientists use subsurface data such as wireline logs and petrophysical data. However, this process is often tedious, repetitive, and time-consuming. This project aims to use machine learning techniques to predict lithology from petrophysical logs, which are direct indicators of lithology.

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This project helps geoscientists quickly identify subsurface rock types, or lithologies, from well data. You provide raw wireline log data, and it outputs predictions for different rock formations like sandstone, claystone, or limestone. Geoscientists in the oil and gas industry who need to interpret geological formations would find this useful.

No commits in the last 6 months.

Use this if you need to automate or accelerate the process of identifying subsurface lithologies from wireline log datasets.

Not ideal if your primary goal is manual, expert-driven petrophysical interpretation or if you lack access to comprehensive wireline log data.

geoscience oil-gas-exploration petrophysics subsurface-interpretation geological-mapping
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

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Stars

23

Forks

5

Language

Jupyter Notebook

License

MIT

Last pushed

Feb 22, 2025

Commits (30d)

0

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