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name: lasio description: | Read, write, and manipulate LAS (Log ASCII Standard) well log files for borehole geophysical and petrophysical data. Use when the agent needs to: (1) Read/parse LAS 1.2 or 2.0 files, (2) Extract well headers or curve data, (3) Convert LAS to DataFrame/CSV/Excel, (4) Create new LAS files from arrays, (5) Modify existing LAS files, (6) Handle problematic or malformed LAS files, (7) Batch process multiple well files. license: MIT metadata: version: 1.0.2 author: Geoscience Skills tags: '["Well Logs", "LAS", "Petrophysics", "Data I/O", "Lasio", "CWLS", "Wireline", "Borehole"]' dependencies: '["lasio>=0.30", "numpy", "pandas"]' complements: '["welly", "petropy", "striplog"]' workflow_role: data-loading skill_type: domain

lasio - LAS Well Log Files

Quick Reference

import lasio

# Read
las = lasio.read("well.las")

# Access data
df = las.df()                    # DataFrame (depth as index)
gr = las['GR']                   # Single curve as numpy array
depth = las['DEPT']

# Well info
well_name = las.well['WELL'].value
uwi = las.well['UWI'].value

# Write
las.write('output.las')

Key Classes

Class	Purpose
LASFile	Main container - holds headers, curves, data
CurveItem	Single curve with mnemonic, unit, data array
HeaderItem	Header entry (mnemonic, unit, value, descr)

Essential Operations

Read and Inspect

las = lasio.read("well.las")
print(las.curves.keys())         # Available curves
print(las.well)                  # Well section headers
print(las.version)               # LAS version info

Access Curve Data

# As numpy arrays
gr = las['GR']
depth = las['DEPT']

# With metadata
curve = las.curves['GR']
print(curve.unit, curve.descr)   # 'GAPI', 'Gamma Ray'

Create New LAS

import numpy as np

las = lasio.LASFile()
las.well['WELL'] = lasio.HeaderItem('WELL', value='Test-1')
las.well['UWI'] = lasio.HeaderItem('UWI', value='12345678901234')

depth = np.arange(1000, 2000, 0.5)
las.append_curve('DEPT', depth, unit='M', descr='Depth')
las.append_curve('GR', gr_data, unit='GAPI', descr='Gamma Ray')
las.write('output.las')

Modify Existing

las = lasio.read("well.las")
las.append_curve('GR_NORM', las['GR'] / 150, unit='V/V')
del las.curves['BAD_CURVE']
las.well['WELL'].value = 'New Name'
las.write('modified.las')

Handle Problematic Files

# Ignore header errors
las = lasio.read("messy.las", ignore_header_errors=True)

# Check null value
null_val = las.well['NULL'].value  # Usually -999.25

Null Value Handling

LAS files use a specific null value (typically -999.25). Always check and handle:

import numpy as np
null_val = float(las.well['NULL'].value)
df = las.df().replace(null_val, np.nan)

Batch Processing

from pathlib import Path

for path in Path('wells/').glob('*.las'):
    las = lasio.read(path)
    df = las.df()
    # Process...

When to Use vs Alternatives

Tool	Best For
lasio	Direct LAS file I/O, header manipulation, format conversion
welly	Higher-level well analysis, curve processing, multi-well projects
dlisio	DLIS/RP66 binary format files (not LAS)

Use lasio when you need low-level control over LAS file reading/writing, need to handle malformed files, or want to programmatically build LAS files.

Use welly instead when you need curve processing (despike, normalize), multi-well project management, or formation tops. Welly uses lasio internally.

Use dlisio instead when your data is in DLIS format. DLIS files are binary, support multi-frame data and array logs -- lasio cannot read them.

Common Workflows

Read, QC, and export well log data

- [ ] Read LAS file with `lasio.read()`, handle encoding if needed
- [ ] Inspect curves: `las.curves.keys()` and well headers
- [ ] Replace null values: `df.replace(null_val, np.nan)`
- [ ] Validate depth range and sample interval
- [ ] Check for missing curves or anomalous values
- [ ] Export to DataFrame with `las.df()` or write cleaned LAS

Common Issues

Issue	Solution
Encoding errors	lasio.read(f, encoding='latin-1')
Missing curves	Check las.curves.keys() first
Header errors	Use ignore_header_errors=True
Wrong null value	Check las.well['NULL'].value

References

• Curve Mnemonics - Standard curve names and units
• Troubleshooting - Common problems and solutions
• LAS File Structure - Detailed format specification

Scripts

• scripts/validate_las.py - Validate LAS file format
• scripts/las_to_csv.py - Convert LAS to CSV
• scripts/merge_curves.py - Merge curves from multiple files