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Complete well log evaluation workflow from LAS/DLIS loading through QC, petrophysical analysis, lithology classification, and visualization. Use when performing formation evaluation from well log data.
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| name | well-log-evaluation |
| skill_type | workflow |
| description | Complete well log evaluation workflow from LAS/DLIS loading through QC, petrophysical analysis, lithology classification, and visualization. Use when performing formation evaluation from well log data. |
| version | 1.0.0 |
| author | Geoscience Skills |
| license | MIT |
| tags | ["Well Logs","Petrophysics","LAS","DLIS","Formation Evaluation","Workflow"] |
| dependencies | ["lasio","welly","petropy","striplog","pyvista"] |
| complements | ["lasio","dlisio","welly","petropy","striplog","pyvista"] |
| workflow_role | analysis |
End-to-end pipeline for formation evaluation, from loading well log files through quality control, petrophysical analysis, lithology classification, and multi-dimensional visualization.
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
| Question | If Yes | If No |
|---|---|---|
| LAS format (.las)? | Use lasio for loading | Check DLIS format |
| DLIS format (.dlis)? | Use dlisio for loading | Check file type |
| Multiple wells or curve QC needed? | Use welly for management | Use lasio directly |
| Full formation evaluation (Sw, phi, Vsh)? | Use petropy | Compute manually with numpy |
| Need lithology column or stratigraphic log? | Use striplog | Skip to visualization |
| 3D well trajectory visualization? | Use pyvista | Use matplotlib for log plots |
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[].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()
from welly import Well, Curve
# Load well with welly (uses lasio internally)
w = Well.from_las('well_A.las')
# Access curves
gr = w.data['GR']
print(gr.start, gr.stop, gr.step)
# Despike gamma ray log
gr_clean = gr.despike(z=2.0) # Remove spikes > 2 std dev
# Normalize to 0-1 range
gr_norm = (gr_clean - gr_clean.min()) / (gr_clean.max() - gr_clean.min())
# Resample curves to common depth basis
df_resampled = w.df(keys=['GR', 'RHOB', 'NPHI', 'RT'], step=0.5)
df_resampled = df_resampled.dropna()
import petropy as ptr
# Load into petropy Log object
log = ptr.Log(las)
# Formation evaluation workflow
# 1. Calculate Vshale from GR
log.formation_multimineral_model()
# Manual Vshale calculation (linear method)
gr = df['GR'].values
gr_clean = np.nanmin(gr) # Sand line
gr_shale = np.nanmax(gr) # Shale line
vshale = (gr - gr_clean) / (gr_shale - gr_clean)
vshale = np.clip(vshale, 0, 1)
# 2. Porosity from density log
rho_matrix = 2.65 # g/cc (quartz)
rho_fluid = 1.0 # g/cc (freshwater)
phi_density = (rho_matrix - df['RHOB']) / (rho_matrix - rho_fluid)
phi_density = np.clip(phi_density, 0, 0.5)
# 3. Water saturation (Archie equation)
a, m, n = 1.0, 2.0, 2.0 # Archie parameters
Rw = 0.05 # Formation water resistivity (ohm-m)
Rt = df['RT'].values # True resistivity
phi = phi_density.values
Sw = ((a * Rw) / (phi**m * Rt))**(1/n)
Sw = np.clip(Sw, 0, 1)
# 4. Permeability (Timur-Coates)
k_timur = 0.136 * (phi**4.4) / (Sw**2) * 1e4 # mD
from striplog import Striplog, Component, Interval
# Build lithology log from Vshale cutoffs
intervals = []
depth = df['DEPT'].values
for i in range(len(depth) - 1):
if vshale[i] < 0.3:
lith = Component({'lithology': 'sandstone'})
elif vshale[i] < 0.6:
lith = Component({'lithology': 'siltstone'})
else:
lith = Component({'lithology': 'shale'})
intervals.append(Interval(top=depth[i], base=depth[i+1], components=[lith]))
strip = Striplog(intervals)
strip = strip.anneal() # Merge adjacent identical intervals
# Display
strip.plot(aspect=10)
import pyvista as pv
# 3D well path with property
trajectory = np.column_stack([
df['X'].values, df['Y'].values, df['DEPT'].values * -1
])
well_path = pv.Spline(trajectory, n_points=len(trajectory))
well_path['GR'] = df['GR'].values
well_path['Porosity'] = phi_density.values
plotter = pv.Plotter()
plotter.add_mesh(well_path, scalars='Porosity', cmap='viridis',
line_width=5, render_lines_as_tubes=True)
plotter.show()
- [ ] Load LAS file with `lasio.read()`, replace null values with NaN
- [ ] Inspect available curves (GR, RHOB, NPHI, RT, DT minimum)
- [ ] QC curves with welly: despike, check ranges, identify washouts (caliper)
- [ ] Calculate Vshale from GR (linear, Larionov, or Clavier method)
- [ ] Calculate porosity from density or neutron-density crossplot
- [ ] Calculate water saturation using Archie or dual-water model
- [ ] Estimate permeability from Timur-Coates or Wyllie-Rose
- [ ] Flag pay zones: phi > cutoff, Sw < cutoff, Vsh < cutoff
- [ ] Generate composite log plot (GR, resistivity, porosity, Sw, pay flag)
- [ ] Export results to LAS or CSV
- [ ] Load multiple LAS files with lasio or welly batch loading
- [ ] Standardize curve mnemonics across wells (GR, GRGC, SGR -> GR)
- [ ] Normalize GR logs to common scale across wells
- [ ] Pick formation tops manually or from Vshale transitions
- [ ] Create striplog for each well with formation intervals
- [ ] Build correlation panel with matplotlib or pyvista
- [ ] Export formation tops to CSV
- [ ] Load LAS file with `lasio.read()`
- [ ] Check depth range, step, and null values
- [ ] Print curve statistics: min, max, mean, NaN count
- [ ] Flag out-of-range values (GR: 0-300, RHOB: 1.5-3.0, NPHI: -0.05-0.6)
- [ ] Check for constant or stuck readings
- [ ] Identify depth intervals with poor data (washout from caliper)
- [ ] Plot all curves for visual inspection
Use the well log evaluation workflow when:
Use individual domain skills when:
lasio alone)dlisio alone)mplstereonet)| Issue | Solution |
|---|---|
| LAS encoding errors | Use lasio.read(f, encoding='latin-1') |
| Curves have different depth sampling | Resample with welly or np.interp |
| Negative porosity values | Clip to 0; check matrix density assumption |
| Sw > 1.0 from Archie | Check Rw, Archie parameters; use clay-corrected model |
| Vshale > 1 in hot shales | Apply non-linear Vshale correction (Larionov) |
| DLIS multi-frame confusion | Iterate f.frames to find correct frame with target channels |