🛢️ DCApy: Python for modeling oil production with statistical rigor using DCA.
If you work in petroleum engineering or energy analysis, you’ll be interested in DCApy. It’s a Python library designed for Decline Curve Analysis (DCA), allowing you to incorporate uncertainty and automate production forecasts.
What does DCApy offer?#
Deterministic and probabilistic modeling#
It allows adding probabilistic variables to assess risks and produce more robust forecasts.
Data validation with Pydantic#
When creating modeling instances, it automatically checks data consistency. (scuervo91.github.io)
Flexible API to store models in the cloud#
Makes it easy to save and retrieve model instances programmatically.
Interactive tutorials included#
The official site offers step-by-step guides: from getting started and ARPS classes, to WOR models and cash flow simulation.
Who is it useful for?#
- Petroleum engineers and geoscientists modeling well production.
- Risk analysts looking to incorporate uncertainty into forecasts.
- Professionals who need a formal, validated, and documented Python tool for DCA.
DCApy is an excellent option for those seeking structured and reliable solutions for oil production modeling.
This library is developed by Santiago Cuervo. I previously helped with some suggestions.
More information at the link 👇
Code at:
