lasio

Read and write Log ASCII Standard files with Python.

This is a Python 3.3+ package to read and write Log ASCII Standard (LAS) files, used for borehole data such as geophysical, geological, or petrophysical logs. It's compatible with versions 1.2 and 2.0 of the LAS file specification, published by the Canadian Well Logging Society. Support for LAS 3 is being worked on. In principle it is designed to read as many types of LAS files as possible, including ones containing common errors or non-compliant formatting.

lasio is primarily for reading & writing data and metadata to and from LAS files. lasio does not mind whether LAS files meet the formal specification before reading data from them; check out the project lascheck for doing that sort of thing. If you are working specifically with lithological or stratigraphic data, you may find striplog helpful, while if you are focused on working at the well level, please take a look at welly, which provides much more functionality in that area.

Note this is not a package for reading LiDAR data (also called "LAS files"); you may want to check out laspy for that.

lasio stopped supporting Python 2.7 in August 2020. The final version of lasio with Python 2.7 support is version 0.26.

Code of conduct

See our code of conduct.

Documentation

See here for the complete lasio package documentation.

Quick start

For the minimum working requirements, you'll need numpy installed. Install lasio with:

$ pip install lasio

To make sure you have everything, use this to ensure pandas, cchardet, and openpyxl are also installed:

$ pip install lasio[all]

Example session:

>>> import lasio

You can read the file using a filename, file-like object, or URL:

>>> las = lasio.read("sample_rev.las")

Data is accessible both directly as numpy arrays

>>> las.keys()
['DEPT', 'DT', 'RHOB', 'NPHI', 'SFLU', 'SFLA', 'ILM', 'ILD']
>>> las['SFLU']
array([ 123.45,  123.45,  123.45, ...,  123.45,  123.45,  123.45])
>>> las['DEPT']
array([ 1670.   ,  1669.875,  1669.75 , ...,  1669.75 ,  1670.   ,
        1669.875])

and as CurveItem objects with associated metadata:

>>> las.curves
[CurveItem(mnemonic=DEPT, unit=M, value=, descr=1  DEPTH, original_mnemonic=DEPT, data.shape=(29897,)),
CurveItem(mnemonic=DT, unit=US/M, value=, descr=2  SONIC TRANSIT TIME, original_mnemonic=DT, data.shape=(29897,)),
CurveItem(mnemonic=RHOB, unit=K/M3, value=, descr=3  BULK DENSITY, original_mnemonic=RHOB, data.shape=(29897,)),
CurveItem(mnemonic=NPHI, unit=V/V, value=, descr=4   NEUTRON POROSITY, original_mnemonic=NPHI, data.shape=(29897,)),
CurveItem(mnemonic=SFLU, unit=OHMM, value=, descr=5  RXO RESISTIVITY, original_mnemonic=SFLU, data.shape=(29897,)),
CurveItem(mnemonic=SFLA, unit=OHMM, value=, descr=6  SHALLOW RESISTIVITY, original_mnemonic=SFLA, data.shape=(29897,)),
CurveItem(mnemonic=ILM, unit=OHMM, value=, descr=7  MEDIUM RESISTIVITY, original_mnemonic=ILM, data.shape=(29897,)),
CurveItem(mnemonic=ILD, unit=OHMM, value=, descr=8  DEEP RESISTIVITY, original_mnemonic=ILD, data.shape=(29897,))]

Header information is parsed into simple HeaderItem objects, and stored in a dictionary for each section of the header:

>>> las.version
[HeaderItem(mnemonic=VERS, unit=, value=1.2, descr=CWLS LOG ASCII STANDARD -VERSION 1.2, original_mnemonic=VERS),
HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=ONE LINE PER DEPTH STEP, original_mnemonic=WRAP)]
>>> las.well
[HeaderItem(mnemonic=STRT, unit=M, value=1670.0, descr=, original_mnemonic=STRT),
HeaderItem(mnemonic=STOP, unit=M, value=1660.0, descr=, original_mnemonic=STOP),
HeaderItem(mnemonic=STEP, unit=M, value=-0.125, descr=, original_mnemonic=STEP),
HeaderItem(mnemonic=NULL, unit=, value=-999.25, descr=, original_mnemonic=NULL),
HeaderItem(mnemonic=COMP, unit=, value=ANY OIL COMPANY LTD., descr=COMPANY, original_mnemonic=COMP),
HeaderItem(mnemonic=WELL, unit=, value=ANY ET AL OIL WELL #12, descr=WELL, original_mnemonic=WELL),
HeaderItem(mnemonic=FLD, unit=, value=EDAM, descr=FIELD, original_mnemonic=FLD),
HeaderItem(mnemonic=LOC, unit=, value=A9-16-49, descr=LOCATION, original_mnemonic=LOC),
HeaderItem(mnemonic=PROV, unit=, value=SASKATCHEWAN, descr=PROVINCE, original_mnemonic=PROV),
HeaderItem(mnemonic=SRVC, unit=, value=ANY LOGGING COMPANY LTD., descr=SERVICE COMPANY, original_mnemonic=SRVC),
HeaderItem(mnemonic=DATE, unit=, value=25-DEC-1988, descr=LOG DATE, original_mnemonic=DATE),
HeaderItem(mnemonic=UWI, unit=, value=100091604920, descr=UNIQUE WELL ID, original_mnemonic=UWI)]
>>> las.params
[HeaderItem(mnemonic=BHT, unit=DEGC, value=35.5, descr=BOTTOM HOLE TEMPERATURE, original_mnemonic=BHT),
HeaderItem(mnemonic=BS, unit=MM, value=200.0, descr=BIT SIZE, original_mnemonic=BS),
HeaderItem(mnemonic=FD, unit=K/M3, value=1000.0, descr=FLUID DENSITY, original_mnemonic=FD),
HeaderItem(mnemonic=MATR, unit=, value=0.0, descr=NEUTRON MATRIX(0=LIME,1=SAND,2=DOLO), original_mnemonic=MATR),
HeaderItem(mnemonic=MDEN, unit=, value=2710.0, descr=LOGGING MATRIX DENSITY, original_mnemonic=MDEN),
HeaderItem(mnemonic=RMF, unit=OHMM, value=0.216, descr=MUD FILTRATE RESISTIVITY, original_mnemonic=RMF),
HeaderItem(mnemonic=DFD, unit=K/M3, value=1525.0, descr=DRILL FLUID DENSITY, original_mnemonic=DFD)]

The data is stored as a 2D numpy array:

>>> las.data
array([[ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       ...,
       [ 1669.75 ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],
       [ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ]])

You can also retrieve and load data as a pandas DataFrame, build LAS files from scratch, write them back to disc, and export to Excel, amongst other things.

See the package documentation for more details.

Contributing

Contributions are invited and welcome.

See Contributing for how to get started.

License

MIT