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)

import lasio
import numpy as np
import pandas as pd

# Load LAS file
las = lasio.read('well_A.las')
df = las.df().reset_index()  # DataFrame with depth as column
null_val = float(las.well['NULL'].value)
df = df.replace(null_val, np.nan)

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

import dlisio

# Load DLIS file (for modern well data)
with dlisio.dlis.load('well_B.dlis') as files:
    f = files[0]
    for frame in f.frames:
        print(frame.name, [ch.name for ch in frame.channels])
    # Extract channels to numpy arrays
    frame = f.frames[0]
    depth = frame.channels[0].curves()
    gr = frame.channels[1].curves()