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. We find Spotfire especially powerful in the subsurface because it allows us to merge ad-hoc completion, production, or attribute data with structured data like seismic and horizons. In doing so, we can better leverage data to make an integrated, rich model of the subsurface environment and its uncertainties.
Further, by leveraging the robust compute engine within Spotfire, we can perform some remarkable calculations and analyses all within the same tool that merges and visualizes the data sets. Ultimately, this means we can run ad-hoc calculations on ad-hoc data; Spotfire becomes a powerful sandbox. The purpose of these posts is to give you an idea of what you can do in this sandbox by leveraging Spotfire, some Ruths.ai extensions, and your ingenuity.
Each post tries to tackle a problem – and along the way – provides a nugget of analytics or calculation that you can apply to your projects. Taken as a whole, they demonstrate how solving these problems can stack and build towards larger, more comprehensive opportunities for improvement and analysis.
We are going to cover:
- Loading and visualizing wellbores and horizons
- Calculating dynamic attributes on a horizon
- Loading and representing seismic data in Spotfire
- Visualizing seismic slices in the heatmap
- Running calculations on seismic values (high pass filter)
- Extracting features from seismic
- Creating well paths from control points
- Annotating well paths with seismic values
- Calculating seismic values over completion intervals
- Annotating well paths with NPT events
- Merging seismic, completion, and production data
- Running multivariate analyses on GnG, DnC, and production data
Development was done on a Surface Pro 3 with 8 GB of memory.
Founder and CEO of Petro.ai