Like a lot of petroleum engineers, I learned Python from tutorials that predicted flower species and house prices — great for syntax, useless for the actual work. The gap between "I can write a for-loop" and "I can load a LAS file, run a decline-curve analysis, and model my field's data" never really got bridged.
So I built the resource I wish I'd had.
Python for Oil & Gas is a free, hands-on course that teaches Python entirely through our problems. Twenty-two chapters take you from the basics to real analysis:
- Well-log / LAS handling and petrophysics
- Decline-curve analysis and production forecasting
- PVT correlations and fluid properties
- Reservoir simulation workflows
- Machine learning applied to petroleum data
Every example runs directly in your browser — no install, no environment setup — on real Niger Delta field data. You read a concept, then run and edit the code right there on the page.
I made all 22 chapters free to read, because the whole point was accessibility: a student on a slow laptop, or an engineer between roles, should be able to pick this up with no paywall and no 2-GB download.
If you work with petroleum data — or you're a student heading that way — I'd genuinely value your feedback, especially on the data-science and ML chapters. Tell me what's useful, what's missing, where it should go deeper.
Read and run it here: https://www.pythonforoilandgas.com
This post reflects a purely personal opinion & not that of the organization with which I am affiliated.