well-log-evaluation

Well Log Evaluation Workflow

End-to-end pipeline for formation evaluation, from loading well log files through quality control, petrophysical analysis, lithology classification, and multi-dimensional visualization.

Skill Chain

lasio/dlisio     welly           petropy         striplog        pyvista
[File I/O]   --> [QC & Prep]  --> [Petrophysics] --> [Lithology] --> [3D Viz]
  |               |                |                 |               |
  LAS parsing     Despike          Vshale calc       Facies log      3D well
  DLIS frames     Normalize        Porosity          Intervals       Fence diagram
  Curve extract   Merge curves     Sw, Perm          Correlation     Property vol

Decision Points

Step-by-Step Orchestration

Stage 1: Data Loading (lasio / dlisio)

The LAS example below assumes depth in metres, GR in API, bulk density in g/cm³, and resistivity in ohm-m. Inspect the headers and convert other units before continuing. The DLIS block is an alternative inspection entrypoint; select a frame, map mnemonics, and supply the same units before Stage 2.

import lasio
import numpy as np
import pandas as pd

# Load LAS file
las = lasio.read('well_A.las')
df = las.df().rename_axis('DEPT').reset_index()
null_val = float(las.well['NULL'].value)
df = df.replace(null_val, np.nan)
if las.curves[0].unit.strip().lower() not in {'m', 'metre', 'meter'}:
    raise ValueError('Convert the depth basis to metres before this example')
if (len(df) < 2 or not np.isfinite(df['DEPT']).all()
        or not np.all(np.diff(df['DEPT']) > 0)):
    raise ValueError('QC the depth basis: require finite, strictly increasing samples')

# Inspect available curves
print(las.curves.keys())  # ['DEPT', 'GR', 'RHOB', 'NPHI', 'RT', 'DT']
well_name = las.well['WELL'].value