logsuite 0.2.1

Petrophysical well log analysis with depth-weighted statistics, hierarchical filtering, and template-driven visualization

logSuite

Fast, intuitive Python library for petrophysical well log analysis. Load LAS files, filter by zones, compute depth-weighted statistics, and create publication-quality log displays—all in just a few lines.

Key Features

• Lazy Loading — Parse headers instantly, load data on demand
• Numpy-Style Operations — well.HC_Volume = well.PHIE * (1 - well.SW)
• Hierarchical Filtering — Chain filters: well.PHIE.filter('Zone').filter('Facies').sums_avg()
• Depth-Weighted Statistics — Proper averaging for irregular sampling
• Multi-Well Analytics — Cross-well statistics: manager.PHIE.filter('Zone').percentile(50)
• Professional Visualization — Customizable well log displays with templates
• Interactive Crossplots — Scatter plots with color/size/shape mapping by property
• Regression Analysis — Linear, polynomial, exponential, logarithmic, and power regression
• Project Persistence — Save/load entire projects with metadata and templates

Installation

pip install logsuite

Requirements: Python 3.10+, numpy, pandas, scipy, matplotlib

Quick Start

1-Minute Tutorial

from logsuite import WellDataManager

# Load and analyze
manager = WellDataManager()
manager.load_las('well.las')

well = manager.well_12_3_4_A
stats = well.PHIE.filter('Zone').sums_avg()

print(stats['Top_Brent']['mean'])  # → 0.182 (depth-weighted)

Three lines to go from LAS file to zonal statistics.

5-Minute Quick Start

Load data:

from logsuite import WellDataManager
import pandas as pd

manager = WellDataManager()
manager.load_las('well_A.las')
manager.load_las('well_B.las')

# Load formation tops from DataFrame
tops_df = pd.DataFrame({
    'Well': ['12/3-4 A', '12/3-4 A', '12/3-4 B'],
    'Surface': ['Top_Brent', 'Top_Statfjord', 'Top_Brent'],
    'MD': [2850.0, 3100.0, 2900.0]
})
manager.load_tops(tops_df, well_col='Well', discrete_col='Surface', depth_col='MD')

Access wells and properties:

well = manager.well_12_3_4_A

phie = well.PHIE
sw = well.SW

print(well.properties)  # ['PHIE', 'SW', 'PERM', 'Zone', ...]
print(well.sources)     # ['Petrophysics', 'Imported_Tops']

Compute statistics:

# Single filter — group by Zone
stats = well.PHIE.filter('Zone').sums_avg()

# Chain filters — hierarchical grouping
stats = well.PHIE.filter('Zone').filter('Facies').sums_avg()

Create computed properties:

well.HC_Volume = well.PHIE * (1 - well.SW)
well.Reservoir = (well.PHIE > 0.15) & (well.SW < 0.35)

# Apply to all wells at once
manager.PHIE_percent = manager.PHIE * 100

Visualize:

from logsuite import Template

template = Template("basic")
template.add_track(
    track_type="continuous",
    logs=[{"name": "GR", "x_range": [0, 150], "color": "green"}],
    title="Gamma Ray"
)
template.add_track(track_type="depth", width=0.3)

view = well.WellView(depth_range=[2800, 3000], template=template)
view.show()

Save your work:

manager.save('my_project/')
manager = WellDataManager('my_project/')  # Load later

Documentation

For detailed guides, API reference, and examples, visit the full documentation:

logsuite.readthedocs.io

• Quick Start — Get up and running
• Loading Data — LAS files, tops, and data import
• Wells & Properties — Property types, filtering, computed logs
• Statistics — Depth-weighted statistics and sums/averages
• Visualization — Well log displays and templates
• Regression — Trend fitting and crossplots
• Multi-Well Analysis — Cross-well workflows
• API Reference — Complete class and method documentation

License

MIT — see LICENSE for details.