Found this interesting article in LinkedIn:
WORKING WITH 3D SEISMIC DATA IN PYTHON USING SEGYIO AND NUMPY (MOSTLY) by Matteo Nicoli. It comes with code, Python notebook and repository.
Keywords: segyio, seismic, python, notebook
Another reproducible example of regression using Python to calculate economic risk. By Matteo Niccoli (2017).
PVT coded in Python!
Keywords: PVT, Python, phase behavior, EOS
I learned Python 10+ ago. With R now in my toolbox, it is difficult to go back. But still I am coding in Python to maintain the old code. What I really dislike is: the Jupyter notebooks, although I fell in love with them at first sight; the acceptance of organized chaos with the multiple versions floating around. I guess you get use to it when you are part of the Py ecosystem.
Somebody asked me earlier if I know of libraries for Production Engineering written in Python. Meaning, open source code for production engineering, production optimization, artificial lift, or gas lift, specifically.
Unfortunately, to my knowledge, there are not open source Python libraries for Production Engineering. Most of the applications or software for optimization and nodal analysis are proprietary requiring fees and and licenses. From that side, the curious petroleum engineer would have to code everything, practically, from scratch.
I used and wrote Python applications for more than 10 years. Then, I started to use R for my data science projects in Petroleum Engineering. I know Python quite well, being one its major weaknesses multiple versions of Python floating around and packages with no “adult” supervision. Anyway, after 2 years of R experience (far too short if you compare it with that of the R experts), this is my take on what makes R better than Python:
The short answer is no. R is not a versatile as Python.
R is a comprehensive statistical, mathematical and scientific tool. Its learning curve could be daunting and intimidating but the effort pays if you deal with data every day.
Python and R are not in the same league either. While Python is a generic developing and prototyping tool, R focus is narrower, focusing on providing sciences and engineering with well thought data analytics functions and packages.
In the last century, the production engineer built the well models one by one and analyzed the results also one by one. With the ubiquity of the personal computer, desktops and laptops, an unimaginable computational power has been put in our hands. But we need the right tools!
The spreadsheet was invented in the 80’s and was a great invention. The beauty of it is that you can produce results right away.
Table of Contents Getting data from a well model using a single well class
Geothermal gradient and downhole equipment arrays
Automatically calculating BHP from WHP
State of the file before BHP calculations
State after BHP calculations
Getting basic well test data from a model
Building a well test dataframe
A dataframe for Downhole Equipment
Basic Statistics for a well test