Category: Production Engineering

Spark lines can show the shape of highly variable data in a small amount of space

In Spotfire Graphical Table visualization, the use of sparklines is a fantastic way to quickly visualize our data in table format. But, what if we have highly variable data in which it would be better to use a logarithmic scale on the Y-axes? Note, there is no option for using a logarithmic scale on the spark line axes visualizations. We have two options here: to use multiple scales or write a custom expression with multiple scales.

One of the main uses of sparklines is to show the “shape” of our data. If our data range is less variable, then a single arithmetic scale for all sparkline axes is fine. However, in the case below we need to use a different arithmetic scale for each spark line in the column to honor the high variability of the data.

Go to Properties of Graphical Table > Axes and select spark line column as seen below. Now select Settings button for that spark line column.

Then, select Axes and change radio button under “Y-axis scale” from “One scale for all sparklines in this column” to “Multiple scales.” We do the same for all spark line columns with highly variable data we want to “compare,” as seen in the second spark line column in the Graphical Table below.

Note the huge improvement in being able to see the “shape” of our data with “Multiple scales” selected, compared to the first visualization above.

Next, if we want a Log or Logarithmic scale, we can easily write a custom expression as seen below by right mouse clicking of Y-axis name in Sparkline Settings.

Insert Log function Custom Expression, then hit Okay.

Compare final Log Scale Graphical Table below to previous two arithmetic Tables above. Note visualization below has “Multiple scales.”

Finally, if we use “One scale for all sparklines in this column” instead of “Multiple scales” the results may not show enough differentiation especially if you have extreme outliers. Compare this last log image with our first arithmetic one.

Including Formation Tops in Well Log Visualization

It is quite easy to include formation tops in the Ruths.ai Well Log Visualization. The neatest way to do that is to have a data table that contains the formation top depth for each well contained in the data table that has the well log data. In its most basic form, the formation tops data table should contain at least 3 columns: Formation Name, Top Depth and Well Name. Here is a video of how to add formation tops to Ruths.ai Well Log Visualization:

Read More

One-Stop Tool for Viewing Subsurface and Well Trajectories in 3D

If you are a petroleum engineer and you have Spotfire installed on your computer, you’re further ahead than you realize. Sound strange? Here’s the hint:

Spotfire can be your tool for:

  • visualizing and analyzing 3D subsurface map and
  • diagramming well trajectory

And you can do all that in one environment.

Now, I’m guessing you thought that those analyses could only be done with some high-end expensive software from say Schlumberger, Halliburton, IHS, or Baker Hughes; not anymore.

Read More

Comparing Average with Values in Same Plot in Spotfire

A common task for an analyst is to plot averaged values in the same chart against quantities of compared variables in order to show the deviation. For instance, a visual representation of salaries of a certain job function in 3 US cities in the last year, can include the US national average to inform viewers of the departure of each city salary from the national average.

Read More

Well Deliverability (IPR/TPR) using Spotfire

The deliverability of a system is its ability to deliver gas as a function of pressure. Ruths.ai Well Deliverability tool is developed to assist oilfield operators in determining the flow rates of gas-drive wells using inflow performance relationship (IPR) and tubing performance relationship (TPR) of reservoir, wellbore and production data.

Read More