As part of the build up for the TIBCO Energy Forum this past August, Troy contributed a piece to TIBCO’s Trends and Outliers blog on what he’s coined as the New Culture: “Domain Data Science”. If you have a chance, take a look.
Out of the box, Spotfire does not naturally load or represent seismic data. In this post, we will cover how we have extended the Spotfire platform with the Segy data source (available as part of the 3D Subsurface Visualization) which allows the loading, representation and visualization of seismic data in Spotfire.
In this post, we will extract the seismic binary data into a vanilla Spotfire table which we can visualize using a heatmap. In the next post, we will actually do some interpretation on the seismic, but for now we wanted to show how you can manipulate seismic data in TERR. We will add some dynamic interaction so the user can select different cross line or inline indices to extract and view in a heatmap. Once the data is extracted and visualized, we can color it, mark it, and build histograms with it. Ok let’s get started.
If any of you have ever sat and watched a live NFL game this year, you are well aware that the new big thing is daily fantasy drafts. Who has time to sit around for a full season anymore to gloat to your friends on how little they know about pigskin? Not us, that’s who! So, I logged in to DraftKings.
Gleaning information from ad-hoc data, answering those questions that create incredible value for an organization has never been easier than when using the innovative approaches provided by Spotfire.
TIBCO Spotfire is a powerful tool that supports the analysis and visualization of ad-hoc data. In this series of posts, we are going to dig into how we can bring subsurface data into Spotfire and then leverage it to solve canonical problems with an innovative and novel tool set.
In 2013, I began working as a data analyst for Ruths Analytics and Innovations, a startup data science company in Houston, focusing primarily on Oil and Gas clients. I was looking for something different, and it was, considering I had been previously developing business intelligence solutions for federal agencies in DC.