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


## Key Classes

| Class | Purpose |
|-------|---------|
| `PhysicalFile` | Container returned by `dlis.load()` |
| `LogicalFile` | Independent dataset within physical file |
| `Frame` | Group of channels with common sampling |
| `Channel` | Individual log curve with metadata |
| `Origin` | Well and file metadata |

## Essential Operations

### Read Curves to DataFrame
```python
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

Object Type	Description
ORIGIN	File/well metadata
FRAME	Channel grouping with index
CHANNEL	Log curve definition
TOOL	Logging tool info
PARAMETER	Constants and settings

Common Curve Names

Curve	Description
DEPT, DEPTH, TDEP	Depth curves
GR	Gamma ray
NPHI	Neutron porosity
RHOB	Bulk density
DT, DTC	Compressional slowness
RT, ILD	Resistivity

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

Feature	DLIS	LAS
Format	Binary	ASCII
Multi-frame	Yes	No
Array data	Yes	Limited
Metadata	Rich	Basic

When to Use vs Alternatives

Tool	Best For
dlisio	Reading DLIS/RP66 binary files, multi-frame data, image logs
lasio	LAS (ASCII) well log files, simpler format, widely supported
welly	Higher-level well data management, curve processing, projects

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

dlisio

dlisio - DLIS/LIS File Reader

Quick Reference