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
Scripts
