dlisio

name: dlisio description: | Read and parse DLIS (Digital Log Interchange Standard) and LIS (Log Information Standard) well log files. Use when Claude needs to: (1) Read/parse DLIS or LIS files, (2) Extract well log curves as numpy arrays, (3) Access file metadata and origin information, (4) Handle multi-frame or multi-file DLIS, (5) Convert DLIS to LAS or DataFrame, (6) Work with RP66 format well logs, (7) Process array or image log data. version: 1.0.0 author: Geoscience Skills license: MIT tags: [Well Logs, DLIS, RP66, Data I/O, Dlisio, Petrophysics, LIS, Wireline] dependencies: [dlisio>=0.3.0] complements: [welly, petropy, striplog] workflow_role: data-loading

dlisio - DLIS/LIS File Reader

Quick Reference

import dlisio

# Open DLIS file (returns generator of logical files)
with dlisio.dlis.load('well.dlis') as (f, *rest):
    frame = f.frames[0]
    curves = frame.curves()

    # Access by channel name
    depth = curves['DEPTH']
    gr = curves['GR']

    # File metadata
    for origin in f.origins:
        print(origin.well_name, origin.field_name)

Key Classes

Essential Operations

Read Curves to DataFrame

import pandas as pd

with dlisio.dlis.load('well.dlis') as (f, *_):
    frame = f.frames[0]
    curves = frame.curves()
    df = pd.DataFrame(curves)
    df.set_index('DEPTH', inplace=True)

Access Channel and Origin Metadata

with dlisio.dlis.load('well.dlis') as (f, *_):
    # Origin metadata
    for origin in f.origins:
        print(f"Well: {origin.well_name}, Field: {origin.field_name}")

    # Channel properties
    for ch in f.frames[0].channels:
        print(f"{ch.name}: {ch.units}, dim={ch.dimension}")

Find Channels Across Frames

with dlisio.dlis.load('well.dlis') as (f, *_):
    # By exact name or regex
    channels = f.find('CHANNEL', '.*GR.*', regex=True)

    # Find frame containing specific channel
    for frame in f.frames:
        if 'GR' in [ch.name for ch in frame.channels]:
            curves = frame.curves()
            break

Handle Array Channels

with dlisio.dlis.load('well.dlis') as (f, *_):
    curves = f.frames[0].curves()
    for name, data in curves.items():
        if data.ndim > 1:
            print(f"{name}: shape = {data.shape}")  # Image/waveform

Common Object Types

Common Curve Names

Error Handling

dlisio.dlis.set_encodings(['utf-8', 'latin-1'])

try:
    with dlisio.dlis.load('file.dlis') as files:
        for f in files:
            curves = f.frames[0].curves()
except Exception as e:
    print(f"Error: {e}")

DLIS vs LAS Comparison

When to Use vs Alternatives

Use dlisio when your data is in DLIS (RP66) format. DLIS files are common from modern logging tools and contain multi-frame, array, and image data that LAS cannot represent.

Use lasio instead when your data is in LAS format. LAS is ASCII-based, simpler, and more widely supported. Convert DLIS to LAS when downstream tools require it.

Use welly instead when you need well-level data management with curve processing, formation tops, and multi-well projects after initial file loading.

Common Workflows

Read and convert DLIS to DataFrame

- [ ] Load file with `dlisio.dlis.load()`, handle encoding if needed
- [ ] List logical files and frames to understand file structure
- [ ] Inspect channels: names, units, dimensions per frame
- [ ] Extract curves from target frame with `frame.curves()`
- [ ] Handle array/image channels separately (ndim > 1)
- [ ] Convert scalar curves to DataFrame with `pd.DataFrame(curves)`
- [ ] Export to CSV or convert to LAS format

References

• DLIS File Structure - RP66 format specification
• Frames and Channels - Working with frames and channels

Scripts

• scripts/dlis_to_las.py - Convert DLIS to LAS format