Motivation I have been lately curious about how the terms data science, machine learning and artificial intelligence evolved over the years in petroleum engineering papers. It is not that machine learning or data science are brand new fields of study; quite the contrary, they have been around for decades, as it has been artificial intelligence.
Last week I was talking with a colleague about some projects that could be deployed using R, and then the topic of well technical potential came up. I got hooked by the term and then decided to explore it a little bit more.
What is not better that going to the OnePetro website and search for papers on “technical potential”. I did and found 132 papers matching the term. Well, I was not going to purchase those 132 papers, you know.
Introduction If you you love searching, finding, reading and collecting papers when working on a particular petroleum engineering topic, keep reading. We will be using some digital tools and stats to keep up with the 21 century. :)
I have just published a new R package called petro.One which website you could see here. All the code, notebooks, datasets, figures, etc., are located in GitHub at this repository. Everything is explained in detail there but I will show here a summary of what the package does.
digging into the papers available in OnePetro is intoxicating. You know a bit - get a piece of the data - and you want to know more and more. That resource could be further developed into accepting comments, notes and highlights from those who have read, or reading, the papers. And of course, assigning subjects and disciplines as categories. In other words, the OnePetro site could turn into giving smarter response to queries.