striplog

striplog - Lithological Logs

Quick Reference

from striplog import Striplog, Interval, Component

# Create from intervals
intervals = [
    Interval(top=0, base=10, components=[Component({'lithology': 'sandstone'})]),
    Interval(top=10, base=25, components=[Component({'lithology': 'shale'})]),
    Interval(top=25, base=40, components=[Component({'lithology': 'limestone'})]),
]
strip = Striplog(intervals)

# Load from file
strip = Striplog.from_csv('lithology.csv')  # Columns: top, base, lithology

# Access and display
print(strip)
strip.plot()
df = strip.to_dataframe()

Key Classes

Class	Purpose
Striplog	Main log container - holds intervals
Interval	Depth interval with top, base, and components
Component	Rock type definition with properties
Lexicon	Rock type dictionary with synonyms
Legend	Visualization styles (colors, patterns)

Essential Operations

Create from CSV

# CSV format: top,base,lithology
strip = Striplog.from_csv('lithology.csv')
strip.plot()

Create from Description Text

from striplog import Striplog, Lexicon

description = """
0.0 - 5.5 m: Fine to medium sandstone
5.5 - 12.0 m: Grey shale with silt laminations
12.0 - 18.5 m: Massive limestone, fossiliferous
"""
strip = Striplog.from_description(description, lexicon=lexicon)

Query and Extract

# Get interval at depth
interval = strip.read_at(z=15)
print(interval.primary.lithology)

# Crop to depth range
subset = strip.crop((10, 30))

# Unique lithologies
lithologies = strip.unique('lithology')

Statistics

# Net-to-gross for specific lithology
ntg = strip.net_to_gross(pattern={'lithology': 'sandstone'})
print(f"Sandstone: {ntg * 100:.1f}%")

# Merge adjacent same-lithology intervals
merged = strip.merge_neighbours()

Well Correlation

import matplotlib.pyplot as plt

wells = [Striplog.from_csv(f'well{i}.csv') for i in range(1, 4)]
fig, axes = plt.subplots(1, 3, figsize=(10, 8), sharey=True)

for ax, well, name in zip(axes, wells, ['Well 1', 'Well 2', 'Well 3']):
    well.plot(ax=ax, legend=legend)
    ax.set_title(name)

plt.tight_layout()
plt.savefig('correlation.png')

Export

strip.to_csv('output.csv')
strip.to_las('output.las')
strip.to_json('output.json')
df = strip.to_dataframe()

Hatch Patterns

Pattern	Code	Typical Use
Dots	...	Sandstone
Dashes	---	Shale
Plus	+++	Limestone
X	xxx	Dolomite
V	vvv	Volcanic

When to Use vs Alternatives

Tool	Best For
striplog	Structured lithology logs, interval-based data, NTG, correlation
welly	Well log curves (continuous data), multi-well projects
custom matplotlib	Simple one-off stratigraphic columns without interval logic

Use striplog when you need to represent geological intervals (lithology, facies) as structured objects with querying, statistics, and correlation.

Use welly instead when working with continuous well log curves (GR, RHOB). Welly and striplog complement each other -- welly for curves, striplog for lithology intervals.

Use custom matplotlib instead for simple, one-off stratigraphic columns where you don't need interval querying or net-to-gross calculations.

Common Workflows

Create lithological log from CSV data

- [ ] Prepare CSV with columns: top, base, lithology (optionally color)
- [ ] Load with `Striplog.from_csv('lithology.csv')`
- [ ] Define Legend with colors and hatch patterns for each lithology
- [ ] Verify intervals: check for gaps or overlaps
- [ ] Merge adjacent same-lithology intervals with `merge_neighbours()`
- [ ] Compute statistics: `net_to_gross()`, `unique()`
- [ ] Plot with legend and export to desired format

Common Issues

Issue	Solution
Text not parsing	Define a Lexicon with synonyms
Wrong colors	Create custom Legend with Decor objects
Gaps in log	Check interval top/base values match
Plot looks wrong	Verify depth direction (increasing down)

References

• Lexicon Configuration - Rock type dictionaries and synonyms
• Component Definitions - Properties and multi-attribute intervals

Scripts

• scripts/create_striplog.py - Create striplog from CSV or text data