Package: lasio
lasioDocumentation(stable)â€¢Documentation(main branch)Read and write Log ASCII Standard files with Python.This is a Python 3.7+ 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 theCanadian Well Logging
Society. Support for LAS 3 isbeing worked on.lasio is primarily for reading and writing data and metadata to and from
LAS files. It is designed to read as many LAS files as possible, including
those containing common errors and non-compliant formatting. It can be used
directly, but you may want to consider using some other packages, depending
on your priorities:wellyis a Python package that
uses lasio for I/O but provides alotmore functionality aimed at working
with curves, wells, and projects. I would recommend starting there in most
cases, to avoid re-inventing the wheel!lascheckis focused on
checking whether your LAS file meets the specifications.lasris an R package which
is designed to read large amounts of data quickly from LAS files; this is
a great thing to check out if speed is a priority for you, as lasio is not
particularly fast.LiDAR surveys are also called "LAS files", but they are quite different and
lasio will not help you -- check outlaspyinstead.lasiostoppedsupporting Python 2.7 in August 2020. The final version of lasio with Python 2.7 support
is version 0.26.Code of conductSee ourcode of conduct.Quick startFor the minimum working requirements, you'll need numpy installed. Install
lasio with:$pipinstalllasioTo make sure you have everything, use this to ensure pandas, chardet, and
openpyxl are also installed:$pipinstalllasio[all]Example session:>>>importlasioYou 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 asCurveItemobjects with associated metadata:>>>las.curves[CurveItem(mnemonic=DEPT,unit=M,value=,descr=1DEPTH,original_mnemonic=DEPT,data.shape=(29897,)),CurveItem(mnemonic=DT,unit=US/M,value=,descr=2SONICTRANSITTIME,original_mnemonic=DT,data.shape=(29897,)),CurveItem(mnemonic=RHOB,unit=K/M3,value=,descr=3BULKDENSITY,original_mnemonic=RHOB,data.shape=(29897,)),CurveItem(mnemonic=NPHI,unit=V/V,value=,descr=4NEUTRONPOROSITY,original_mnemonic=NPHI,data.shape=(29897,)),CurveItem(mnemonic=SFLU,unit=OHMM,value=,descr=5RXORESISTIVITY,original_mnemonic=SFLU,data.shape=(29897,)),CurveItem(mnemonic=SFLA,unit=OHMM,value=,descr=6SHALLOWRESISTIVITY,original_mnemonic=SFLA,data.shape=(29897,)),CurveItem(mnemonic=ILM,unit=OHMM,value=,descr=7MEDIUMRESISTIVITY,original_mnemonic=ILM,data.shape=(29897,)),CurveItem(mnemonic=ILD,unit=OHMM,value=,descr=8DEEPRESISTIVITY,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=CWLSLOGASCIISTANDARD-VERSION1.2,original_mnemonic=VERS),HeaderItem(mnemonic=WRAP,unit=,value=NO,descr=ONELINEPERDEPTHSTEP,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=ANYOILCOMPANYLTD.,descr=COMPANY,original_mnemonic=COMP),HeaderItem(mnemonic=WELL,unit=,value=ANYETALOILWELL#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=ANYLOGGINGCOMPANYLTD.,descr=SERVICECOMPANY,original_mnemonic=SRVC),HeaderItem(mnemonic=DATE,unit=,value=25-DEC-1988,descr=LOGDATE,original_mnemonic=DATE),HeaderItem(mnemonic=UWI,unit=,value=100091604920,descr=UNIQUEWELLID,original_mnemonic=UWI)]>>>las.params[HeaderItem(mnemonic=BHT,unit=DEGC,value=35.5,descr=BOTTOMHOLETEMPERATURE,original_mnemonic=BHT),HeaderItem(mnemonic=BS,unit=MM,value=200.0,descr=BITSIZE,original_mnemonic=BS),HeaderItem(mnemonic=FD,unit=K/M3,value=1000.0,descr=FLUIDDENSITY,original_mnemonic=FD),HeaderItem(mnemonic=MATR,unit=,value=0.0,descr=NEUTRONMATRIX(0=LIME,1=SAND,2=DOLO),original_mnemonic=MATR),HeaderItem(mnemonic=MDEN,unit=,value=2710.0,descr=LOGGINGMATRIXDENSITY,original_mnemonic=MDEN),HeaderItem(mnemonic=RMF,unit=OHMM,value=0.216,descr=MUDFILTRATERESISTIVITY,original_mnemonic=RMF),HeaderItem(mnemonic=DFD,unit=K/M3,value=1525.0,descr=DRILLFLUIDDENSITY,original_mnemonic=DFD)]The data is stored as a 2D numpy array:>>>las.dataarray([[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 apandasDataFrame, build LAS files
from scratch, write them back to disc, and export to Excel, amongst other
things.See thepackage documentationfor
more details.ContributingContributions are invited and welcome.SeeContributingfor how to get started.LicenseMIT