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.
So happy to find that OnePetro has improved its search algorithms. Now, lowercase, uppercase and dashed keywords return the same results. This wasn’t the case few months ago. You can see the results below.
This is a search of papers using R, #rstats, and the package petro.One. You could see that “neural network”, “neural-network, and”NEURAL NETWORK“, return the same number of papers: 3197. The same with”data driven" and “data-driven”. This makes the paper search much more efficient and fast.
One of the little annoyances while doing paper research in OnePetro is knowing the correct spelling of the keywords under search. It would seem insignificant but we will see in this article choosing the right keyword could have effects on the results. Let’s see a practical example.
For this demonstration I will use the R package petro.One. It is available from CRAN, as free and open source project. The advantage of using petro.
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.
Neural Networks papers in OnePetro It is incredible the number of papers on Neural Networks contributed by the petroleum engineering community: an astonishing total of 2,918 papers that mention the keyword “neural networks” . And that’s only Conference Papers.
From all these papers, 534 have “neural networks” in their title and they go back as far in time as 1975 (one paper), and start with force by the end of the 80’s (7 papers).