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')