LASMnemonicsID

Well log mnemonic identification and standardization for LAS, DLIS, and ASCII formats

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Features

• Multi-format support: LAS, DLIS, ASCII/CSV/TXT/DAT
• Automatic mnemonic standardization: GR, RHOB, NPHI, DT, SP, CALI, RT, etc.
• Batch processing: Parse entire directories recursively
• Customizable naming: Override default standard names
• Case-insensitive extensions: Works with .las/.LAS, .dlis/.DLIS, .csv/.CSV, etc.
• Pandas integration: Returns clean DataFrames ready for analysis

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Installation

pip install lasmnemonicsid

This installs support for all formats (LAS, DLIS, ASCII/CSV/TXT).

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Quick Start

LAS Files

from LASMnemonicsID import parseLAS

# Parse single LAS file
df = parseLAS("well.las")
print(df.head())

# Parse directory
data = parseLAS("/path/to/las/files/")
for filename, df in data.items():
    print(f"{filename}: {df.shape}")

DLIS Files

from LASMnemonicsID import parseDLIS

# Parse single DLIS file
df = parseDLIS("well.dlis")
print(df.columns)

# Parse directory
data = parseDLIS("/path/to/dlis/files/")

ASCII/CSV/TXT Files

from LASMnemonicsID import parseASCII

# Parse CSV
df = parseASCII("well_log.csv", depth_col="DEPTH")

# Parse tab-separated TXT
df = parseASCII("well_log.txt", delimiter="\t")

# Parse directory
data = parseASCII("/path/to/csv/files/")

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Advanced Usage

Custom Preferred Names

preferred = {
    "deepres": "RT",
    "deepres_preferred_original": "AT90",
    "gamma": "GR"
}

df = parseLAS("well.las", preferred_names=preferred)

Batch Processing

from pathlib import Path

dir_path = Path("/data/wells/")
data = parseLAS(dir_path, verbose=True, preferred_names=preferred)

for fname, df in data.items():
    print(f"{fname}: {df.shape}")
    print(df.head(3))

Mixed Format Directories

las_data = parseLAS("/data/wells/")
dlis_data = parseDLIS("/data/wells/")
ascii_data = parseASCII("/data/wells/")

all_data = {**las_data, **dlis_data, **ascii_data}

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Supported Mnemonics

The package automatically standardizes these curve types:

Curve Type	Standard Name	Example Aliases
Gamma Ray	GR	gr, cggr, cgr, gam, gamma, gammaray, grc, grd, hgr, sgr, lgr, pgr
Spontaneous Potential	SP	sp, idsp, spr, spl, spdl, spdhp, spc, sp0, sp1, cgsp, dlsp
Caliper	CALI	caliper, calip, cal, dcal, acal, cala, cald, cale, calh, hcal, xcal, ycal
Deep Resistivity	RT	rt, rtao, rt90, ild, idph, rild, rd, ae90, at90, atrt, lld, lldc, res, resd
Shallow Resistivity	RXO	rxo, rxoz, msfl, mcfl, sflcc, mgl, m1rx, r40o, aht10
Density	RHOB	rhob, rhoz, den, denb, denc, hrho, hrhob, zden, hden, denf, denn
Density Correction	DRHO	dcor, dcorr, dc, decr, drh, zcor, zcorr, hhdr, denscorr
Neutron Porosity	NPHI	cn, phin, cnc, cns, hnphi, nphi, npor, cncc, nprl, neut, neutpor
Sonic (Compressional)	DT	dt, dtc, dtco, dtcomp, deltat, slow, slowness, tt, ac, acco, delt, dtcomp
Sonic (Shear)	DTS	dts, dtsh, dtsm, dtsc, dtsd, dtsqi, dtshear, deltas, tts, stt, dtshear
Photoelectric Factor	PEF	pe, pef, pefz, pdpe, pedf, pedn, hpedn, pe2, pef8, lpe

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Testing

pytest tests/ -v
pytest tests/test_las.py -v
pytest tests/test_dlis.py -v
pytest tests/test_ascii.py -v

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API Reference

parseLAS(input_path, verbose=True, preferred_names=None)

Parse LAS file(s) and standardize mnemonics.

Parameters:

• input_path (str/Path): LAS file or directory
• verbose (bool): Print parsing info
• preferred_names (dict): Custom name mappings

Returns: DataFrame (single file) or dict (multiple files)

parseDLIS(input_path, verbose=True, preferred_names=None)

Parse DLIS file(s) and standardize mnemonics.

Parameters:

• input_path (str/Path): DLIS file or directory
• verbose (bool): Print parsing info
• preferred_names (dict): Custom name mappings

Returns: DataFrame (single file) or dict (multiple files)

parseASCII(input_path, verbose=True, preferred_names=None, depth_col="DEPTH", delimiter=",")

Parse ASCII/CSV/TXT file(s) and standardize mnemonics.

Parameters:

• input_path (str/Path): ASCII file or directory
• verbose (bool): Print parsing info
• preferred_names (dict): Custom name mappings
• depth_col (str): Name of depth column
• delimiter (str): Field separator

Returns: DataFrame (single file) or dict (multiple files)

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How to Cite

APA

Nobleza Energy. (2026). LASMnemonicsID: Well log mnemonic identification for LAS, DLIS, and ASCII formats [Software]. GitHub. https://github.com/Nobleza-Energy/LASMnemonicsID

BibTeX

@software{LASMnemonicsID,
  author = {Nobleza Energy},
  title = {LASMnemonicsID: Well log mnemonic identification for LAS, DLIS, and ASCII formats},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/Nobleza-Energy/LASMnemonicsID}
}

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License

MIT License - see LICENSE file.

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Contributing

Contributions welcome! Submit a Pull Request.

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Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Made with ❤️ by Nobleza Energy