Type Curve generators are standard in reservoir engineering workflows. They are also a bit problematic. They require lots of inputs, which are time-consuming to build and fill out. You can’t easily recreate them in other projects. Therefore, my next two posts will show you how to make type curve inputs more efficient. The first post will demonstrate how to populate type curve inputs with IronPython and toggle between different sets of inputs. The second post will go further by allowing users to load type curve settings in a table. Then, the user tells an R function which row to pull from the table and place in the type curve inputs. Read on for these great solutions!
This week’s post touches on a subject I’ve never written about before — data connections. More specifically, I’m going to explain how to build Spotfire data connections with a service account. Building data connections with a service account will allow you to create enterprise data connections that aren’t dependent on a single user’s credentials. Multiple users will be able to use data connections stored in the Spotfire library. If that catches your interest, read on.
- Have you noticed key columns for linked data in Data Table Properties and just want to know more?
- Have you tried to use replace value transformations but found options grayed out (screenshot above)?
- Have you ever lost the marked data behind tags when opening or closing a file?
- Would you like to be able to edit values in a data table?
If any of these apply, read on to find how what key columns are and how to use them.
I’ve never found a great way to explain what I do. Analogies help right? The first analogous thing that pops into my head is a Rubix cube. My day to day work feels like trying to solve a Rubix cube over and over, and the cube is a big pile of dirty, messy data. Thus, much of my day to day is troubleshooting data wrangling problems.
Some days, it’s incredibly satisfying, as solving a Rubix cube is. Other days, I want to rip the stickers off and throw it out the window. When I drafted this post, I was in “rip the stickers off” mode. I was frustrated with how long it took to solve data wrangling problems. When I get in that mindset, I want to share what I learn so other people don’t suffer as much. Thus, this post discusses my top 5 ways to approach troubleshooting data problems. These methods will apply no matter what application you are working in or if the problem is related to data wrangling or logic problems. Read on for details.