This is a part of the response I wrote to a reader in my [blog](http://blog.oilgainsanalytics.com/blog/. The question was about the limited scope of the Volve license, questioning its openness, because it doesn’t cover commercialization from the data. This was my response:
I am not finding any problem whatsoever. For my purposes of research, learning, education and teaching, I think the license is alright. In all articles and papers I always give the corresponding attribution.
Introduction Python and R offer a good combination of powers: dozens of proven engineering, data science, and machine learning libraries, also a science oriented approach towards full reproducibility. As I have told you before, I started my coding journey with Python many years ago. I even wrote a large application for production optimization using OpenServer, Prosper, GAP and MBAL by Petroleum Experts while I was on my 3-year tour with Petronas in Kuala Lumpur in Malaysia.
Introduction This time we will be exploring drilling data that is stored using the industry standard WITSML. This format is widely used in the industry in drilling, completion and intervention operations, specifically for real-time surveillance. WITSML stands for Wellsite Information Transfer Standard Markup Language. It has a series of rules to save the data as a consistent schema but essentially is XML. Software developers and IT professionals in the oil industry know it very well.
The Eclipse reservoir models from the Volve dataset working like a charm. The compressed file is 399 MB in size.
I was able to open the models with ResInsight (thank you Matthew Kirkman). The software is open source and relatively easy to use.
Here is the Eclipse case opened.
Let’s enter the individual size of each of the Volve data files:
# megabytes cat(sprintf("Megabytes\n")) #> Megabytes (mega <- c(geophysics = 99, reservoir_eclipse = 390, well_technical = 212, seismic_vsp = 95, production = 2, reports = 162)) #> geophysics reservoir_eclipse well_technical seismic_vsp #> 99 390 212 95 #> production reports #> 2 162 # gigabytes cat(sprintf("Gigabytes\n")) #> Gigabytes (giga <- c(geoscience = 54.6, reservoir_rms = 2.1, well_logs = 6.9, seismic_4d = 330.