well-log-analysis

Skill: Well Log Analysis

Read, analyze, and manipulate well log data in LAS format using lasio library.

Purpose

Handle well log operations across all oil & gas pipeline skills. Provides code patterns for reading LAS files, extracting metadata, performing petrophysical calculations, and exporting data.

Reference Materials

Primary source: lasio documentation

Library: lasio

Related packages:

• welly - Extended functionality for curves, wells, projects
• lascheck - LAS specification validation

Dependencies

pip install lasio pandas matplotlib numpy

Capabilities

1. Read LAS Files

import lasio

# Read LAS file
log = lasio.read('well_log.las')

# Access well metadata
print(f"Well: {log.well['WELL'].value}")
print(f"Field: {log.well['FLD'].value if 'FLD' in log.well else 'N/A'}")
print(f"Company: {log.well['COMP'].value if 'COMP' in log.well else 'N/A'}")

# Access curve data
print(f"Curves: {[curve.mnemonic for curve in log.curves]}")
print(f"Depth range: {log.index_min} to {log.index_max} {log.index_unit}")

2. Export to DataFrame

import lasio
import pandas as pd

log = lasio.read('well_log.las')

# Convert to pandas DataFrame
df = log.df()

# Access specific curves
depth = df.index
gamma_ray = df['GR']
density = df['RHOB']
neutron = df['NPHI']

print(df.head())

3. Access Header Metadata

import lasio

log = lasio.read('well_log.las')

# Version section
print(f"Version: {log.version['VERS'].value}")
print(f"Wrap: {log.version['WRAP'].value}")

# Well section
print(log.well)

# Parameters section
print(log.params)

# Other section
for item in log.other:
    print(item)

# Curves section
for curve in log.curves:
    print(f"{curve.mnemonic}: {curve.unit} - {curve.descr}")

4. Petrophysical Calculations

import lasio
import numpy as np

log = lasio.read('well_log.las')
df = log.df()

# Calculate porosity from density
# Phi = (matrix - bulk) / (matrix - fluid)
matrix_density = 2.65  # g/cc (sandstone)
fluid_density = 1.0    # g/cc (water)

bulk_density = df['RHOB'].values
porosity = (matrix_density - bulk_density) / (matrix_density - fluid_density)
porosity = np.clip(porosity, 0, 0.5)  # Clamp to reasonable range

print(f"Average porosity: {porosity.mean():.2%}")

# Calculate water saturation (Archie equation)
# Sw = ((a * Rw) / (Phi^m * Rt))^(1/n)
a = 1.0
m = 2.0
n = 2.0
Rw = 0.1  # ohm-m (formation water resistivity)

phi = porosity
Rt = df['RT'].values  # true resistivity

Sw = ((a * Rw) / (phi**m * Rt))**(1/n)
Sw = np.clip(Sw, 0, 1)

print(f"Average water saturation: {Sw.mean():.2%}")

# Calculate shale volume from gamma ray
GR = df['GR'].values
GR_min = GR.min()
GR_max = GR.max()

Vsh = (GR - GR_min) / (GR_max - GR_min)
Vsh = np.clip(Vsh, 0, 1)

print(f"Average shale volume: {Vsh.mean():.2%}")