Agent skill
welly
Subsurface well data analysis toolkit for loading, processing, and analyzing well logs, projects, and formation tops. Built on lasio with enhanced curve processing. Use when Claude needs to: (1) Load wells from LAS files with metadata, (2) Work with multi-well Projects, (3) Process curves (despike, smooth, resample, normalize), (4) Manage formation tops, (5) Export well data to DataFrame/LAS/CSV, (6) Perform cross-well analysis and QC.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/welly
SKILL.md
welly - Well Data Analysis
Quick Reference
from welly import Well, Project
# Load single well
w = Well.from_las('well.las')
# Access data
df = w.df() # DataFrame
gr = w.data['GR'] # Curve object
values = gr.values # numpy array
depth = gr.basis # depth array
# Well info
print(w.name, w.uwi)
print(w.data.keys()) # Available curves
# Load multiple wells
p = Project.from_las('wells/*.las')
for well in p:
print(well.name)
Key Classes
| Class | Purpose |
|---|---|
Well |
Single well with curves, location, tops |
Project |
Collection of wells for multi-well workflows |
Curve |
Log curve with depth basis, units, and processing methods |
Essential Operations
Access Curve Data
gr = w.data['GR']
print(gr.mnemonic, gr.units) # Metadata
print(gr.start, gr.stop, gr.step) # Depth range
Process Curves
gr = w.data['GR']
# Clean and filter
gr_clean = gr.despike(window=5, z=2)
gr_smooth = gr.smooth(window=11)
# Transform
gr_norm = gr.normalize() # 0-1 range
gr_resampled = gr.resample(step=0.5)
gr_clipped = gr.clip(top=1500, bottom=2000)
Work with Formation Tops
w.tops = {
'TopFormationA': 1500.0,
'TopFormationB': 1750.0,
}
for name, depth in w.tops.items():
print(f"{name}: {depth} m")
Multi-Well Project
from welly import Project
p = Project.from_las('wells/*.las')
print(f"Loaded {len(p)} wells")
# Filter and analyze
for w in p:
if 'GR' in w.data:
print(f"{w.name}: GR mean={w.data['GR'].values.mean():.1f}")
Export Data
# To DataFrame
df = w.df()
# To LAS file
w.to_las('output.las')
# To CSV
df.to_csv('well_data.csv')
Common Curve Mnemonics
| Mnemonic | Description | Units |
|---|---|---|
| GR | Gamma Ray | GAPI |
| NPHI | Neutron Porosity | v/v |
| RHOB | Bulk Density | g/cc |
| DT | Sonic | us/ft |
| RT/ILD | Deep Resistivity | ohm.m |
| CALI | Caliper | in |
Tips
- Use Project for multi-well workflows - easier than managing individual files
- Check units - welly tracks units, ensure consistency
- Despike before analysis - remove outliers with
curve.despike() - Resample to common basis - use
curve.resample()for cross-well comparison - welly extends lasio - all lasio functionality available
When to Use vs Alternatives
| Tool | Best For |
|---|---|
| welly | Multi-well projects, curve processing, formation tops management |
| lasio | Low-level LAS file I/O, header manipulation, malformed files |
| petropy | Petrophysical calculations (Vsh, porosity, Sw, permeability) |
Use welly when you need to manage wells as objects with curves, tops, and metadata -- especially for multi-well QC and cross-well analysis via Project.
Use lasio instead when you only need to read/write LAS files, handle malformed headers, or need fine control over LAS formatting.
Use petropy instead when your focus is formation evaluation calculations (shale volume, porosity, water saturation) rather than data management.
Common Workflows
Load and QC a multi-well project
- [ ] Load wells with `Project.from_las('wells/*.las')`
- [ ] Check well count and names: `len(p)`, iterate wells
- [ ] Verify required curves exist in each well (`'GR' in w.data`)
- [ ] Despike and clean noisy curves: `curve.despike()`
- [ ] Resample to common depth basis for cross-well comparison
- [ ] Compute summary statistics per well (mean, min, max)
- [ ] Export cleaned data to LAS or DataFrame
References
- Curve Processing - Despike, smooth, normalize, resample methods
- Project Workflows - Multi-well analysis patterns
Scripts
- scripts/well_qc.py - QC well data for gaps and issues
- scripts/project_stats.py - Compute project-level statistics
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