A few years ago, I needed to find as much LAS data for wells in Texas as I could. There isn’t much public data available for wells in Texas, but the University of Texas, endowed with a significant land grant, does provide free public access to a lot of wireline log data for wells on their lands. The catch is that it is only accessible via FTP. Now, I love using python for everything as much as the next guy, but sometimes python is not the right tool for the job.
Every once in a while I receive a well log or mud log from a client in multipage-pdf form. In other words, rather than a single, very tall raster image (in tiff format, for example), the image has been cut up into many, say, 8.5x11-inch or A4 pages and saved in pdf format. Instead, I often want a file as a single, long page in tiff format for use in Petra or to send off to a digitizer.
Matrix identification Matrix identification (MID) refers to a lithology solution in petrophysics where the bulk density and photoelectric factor are used to estimate the lithologies of the rocks being logged. When a standard triple combo log is run in a well, the tool is utterly naive to the true identity of the rocks it encounters. The bulk density for example really measures electron density, and the neutron log (grossly simplified) measures hydrogen, and neither has any clue about what lithology it is looking at.
I’d put up with my old trusty Nexus 4 for long enough. The coup de grâce was a drop to the basement floor which left half the screen unresponsive. So I started searching for a replacement. My requirements were pretty basic. I hoped to find a phone that was officially supported by LineageOS (a stable and fantastic fork of AOSP). The Lineage version I had been running on my Nexus 4 (namely 14.
When working with well log data in a pandas DataFrame, it is very likely that you’ll want to explore your data in the context of geologic zones. By adding zone labels to each row of your DataFrame, it is possible to use some of the fun and powerful features of pandas, like groupby() for stats aggregations. And while the process for adding tops to a DataFrame is not obvious, it is simple.
What follows is a mostly unedited re-submission of a post I made in 2006 on the now-defunct Assistants-in-France forum. Indeed that year I went through the ordeal of buying a car in France. At the time, as far as I could tell there was no guide like this already out there. By now there may very well be dozens of walkthroughs of the process on the internet. Some of the information in this post is useful, and some of it quaint (“calling cards”!
Introduction A caliper log records the borehole size. When a logging tool is pulled up the well, a caplier arm opens or closes as it encounters zones of washout, mudcake, or zones of stable hole condition. If there is abundant washout or mudcake, many logging tools (bulk density, neutron, etc.) do not collect valid petrophysical data. Often a petrophysicist will use some cutoff (2.5 inches of washout, maybe) to determine whether they can use the data over that interval in their analysis or if they should instead throw it out.