Introducing: NPT statistics

In our ElasticSearch database, we merge together large datasets from multiple sources. This allows the user to discover brand new connections and extract useful information from completely new areas. We recently expanded it by accepting NPT data from a number of data sources.
NPT events usually come with a depth tag, which allows us to visualise where in the well they occurred. If they don't have a depth associated with them, we look for a time tag, and run that against the well's time-depth curve (if present) and are able to get an estimated depth from that:

Knowing where in the well the event happened opens up an entirely new alley of analyses, as we already know everything there is to know about each depth point in the well, and to properly honour that fact, we will soon be introducing an entirely new tab in our statistics view dedicated to NPT statistics.

Here are just a few examples of questions you will be able to find answers to when you combine NPT statistics with existing features like filters and our ML similarity cluster:

Which sectionTypes caused the most NPT events in deviated (Inclination > 50) wells?

Looking at these well bores with similar trajectories (found with similarity cluster: https://prowellplan.com/news/see-the-big-picture-in-well-planning-with-machine-learning), does smaller bitSizes appear to cause less NPT in the horisontal section (Filters: Inclination > 50, md > 3000m) ?

These 5 wells have similar trajectories, and similar casing designs, but one has significantly less NPT than the others. How are they different? Does that difference appear to be significant when I look at all wells globally, or is it a red herring?

...We tend to get really productive feedback from our users; we will surely get loads of useful suggestions for new plots to create, once they see the endless possibilities. This is just the beginning!

Example plots below.