The Role of Python in the Oil & Gas Industry
summary
Python automates sensitivity and uncertainty workflows rather than just showing examples: a text‑file model generates 800 simulations automated from parameter grids, a Jupyter notebook streamlines messy SCADA preprocessing via a shared PFS library on GitHub, and analysis uses actual sampling rates (notably 86 second sampling) to detect slugging with periods of slug frequency 6.5–10 minutes. A tuned 1D physical model reproduces half‑year residuals with zero mean, and a hybrid ML/physical approach is proposed to speed tuning.
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chapters (9)
- 0:00Introduction
- 1:07Python in oil and gas
- 6:55Real-world Python use cases
- 9:07Data validation workflow
- 10:51Pipeline sizing simulation
- 16:33Slugging frequency analysis
- 20:53Gas condensate network modeling
- 25:56Python in oil and gas modeling
- 28:02Q&A
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