NoraResearchLab/Lithology-Training-Dataset

Lithology Training Dataset

A supervised training dataset for machine learning and AI systems that learn to identify lithology from well-log data.

This dataset contains exactly 400 wells with standardized wireline-log measurements and corresponding lithological labels.

Training dataset:
https://huggingface.co/datasets/NoraResearchLab/Lithology-Training-Dataset

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Overview

The core task is:

Given a sequence of well-log measurements across depth, predict the lithology present at each depth interval and/or reconstruct the corresponding lithological sequence.

The dataset is designed for:

• Lithology classification
• Well-log interpretation
• Sequence modelling
• Lithological interval segmentation
• Petrophysical machine learning
• Geoscience AI
• Subsurface representation learning
• Benchmark and model development

This dataset is intended for training and model development.

It is separate from the independent evaluation benchmark.

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Dataset Size

Property	Value
Total wells	400
Lithological classes	8
Primary data type	Wireline well logs
Label type	Rule-derived supervised labels
Sampling	Standardized depth grid
Primary task	Lithology identification

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Lithology Classes

The dataset contains:

• SHALE
• SANDSTONE
• LIMESTONE
• DOLOMITE
• ANHYDRITE
• SALT
• COAL
• UNKNOWN/MIXED

UNKNOWN/MIXED represents intervals where the available log evidence does not provide sufficient confidence for assigning one of the principal lithological classes.

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Dataset Configurations

logs

Depth-indexed wireline-log measurements.

Typical structure:

well_id
depth_ft
GR
RHOB
NPHI
PEF
DT
RT
CALI
...