Two of the challenges that upstream majors face include: How to make subsurface data easier to search/discover and how to automate geophysical and petrophysical workflows. Doing either of these manually is time-consuming, costly, and usually done in separate systems. This is where machine learning comes in.
Based on the latest interviews and discussions with geoscientists, here is the most common visualization workflow requested. This basic workflow is important to start automating calculations with machine learning: select some relevant data, select computational functions and run it, and visualize the output—all done from the same system.
Subsurface Digital Workspace Building Blocks
The illustration shows the building blocks that are necessary to fully create a viable subsurface digital workspace.
Each component is essential to deliver great user experience and adoption.
In this illustration, IVAAP provides the UI and UX for visualization of G&G and petrophysical data (well, seismic, etc…).
IVAAP integrates with both an existing search engine—for instance, based on Elastic Search—and multiple data sources on-premise or in the cloud (AWS, Azure, Google, IBM).
From a single point of access, geoscientists can search, QC the data, and select it to launch an algorithm, ML programs, etc.
IVAAP integrates with machine learning and artificial intelligence to embed it into your workflows.