Build your own Well log applications on containers
As with so many folks in the Gulf Coast, I’ve spent some time in the tech trenches of the Oil and Gas (O&G) Industry. I’ve used Recall and Techlog to support geologists and geophysicists in their pursuit of analyzing subsurface formations using well log data, particularly during my time with Petris (now part of Halliburton Landmark). Coming back to O&G; after spending a few years in the DoD space, I began looking for innovative projects to help build a modern application for viewing and working with well logs.
Using all of my Google skills — seriously I searched “open source well logs” and voila, within the first page of results I found this gem — https://github.com/softwareunderground/awesome-open-geoscience — and within it was a project called welly. The amazing team at Agile Scientific created welly, a python library to facilitate the “loading, processing, and analysis of subsurface wells and well data, such as striplogs, formation tops, well log curves, and synthetic seismograms” (quoted from Agile Scientific). Built on top of the hard work done by the lasio Project, Agile created a nice intermediary layer that simplifies many common steps of working with .LAS files for analyzing well log data.
In case you need a refresher on the well log curve mnemonics, please refer to the reliable Schlumberger Curve Mnemonic Dictionary.
Welly offers some higher level functions for rendering the well log curves, and even some quality functions for generating synthetic well log curves for missing data and some basic spikes and gaps checks. I should also state that the Welly project is licensed under the Apache License 2.0. If you want to contribute, please do, or if you want to fork the project, please do that as well. In either case, I recommend pushing any improvements back to the Agile team in the form of pull requests (PR’s).
Red Hat has been able to use both the JupyterHub on Openshift and OpenDataHub.io projects to run welly python applications and notebooks as a container, securely, and at scale, for large data science teams. Having the ability to run the well log application in any environment (on-premise or cloud) is also a huge architectural advantage. As an application owner, I’m not impacted by strategy changes for application infrastructure or data sovereignty issues as I can run my well log applications where the data rests.
To learn more about how Agile Scientific can help your organization, please go visit them at https://agilescientific.com/training.
To learn about how OpenShift can help your organization, and in particular, folks in the Energy industry, go to https://commons.openshift.org/sig/OpenShiftEnergy.html
Thanks to Andy Block and to Kim Archer for Little Brown Booking the hell out of this post.
