This Smart Visualization Platform Challenge (also referred to as a competition) is part of the U.S. Department of Energy’s (DOE’s) SMART Initiative — Science-informed Machine-learning to Accelerate Real Time Decisions in the Subsurface.
The SMART Initiative is led by DOE’s Office of Fossil Energy (FE), and it consists of this prize and multiple research efforts conducted by technical teams of scientists and engineers across 15 different research organizations. The SMART Initiative targets innovative and novel breakthroughs in understanding the subsurface environment through machine learning. The technical teams are developing machine learning based methods to extract knowledge from data, and the prize will be awarded to challengers that develop
the platform for conveying this knowledge.
Many aspects serve as the driver for the SMART Initiative. Decisions on subsurface operations are hindered by the inherent uncertainty and complexity in subsurface systems. By developing an intuitive and deeper understanding of these subsurface systems, a decision maker can choose options that reduce risk and increase efficiency in
the utilization of resources such as unconventional hydrocarbon reservoirs or saline formations for the long-term storage of CO2. Such decisions may be better informed through an experiential visualization environment that facilitates an understanding of (i) the characteristics of the subsurface at multiple scales, (ii) the predicted behavior of the subsurface in response to human actions such as injection or extraction of fluids through wellbores, and (iii) the way(s) in which multiple subsurface phenomena may interact.
Current approaches to visualizing subsurface data present information in formats traditionally used by scientists and engineers as part of a rigorous, physics-based analytical workflow. These formats require expert training to extract insights, and the workflows are time consuming and costly. These latter limitations are being addressed by the technical efforts in the SMART Initiative through new machine-learning based
workflows to extract knowledge from data. The prize is intended to address limitations requiring significant training and expertise by users by presenting the extracted knowledge in an accessible and intuitive way.
We envision an innovative, immersive, and experiential visualization tool (a “visualization platform”) that non-expert decision makers can use to interact directly with subsurface data. Further, this visualization platform can be used by technical experts to enhance their analysis and interpretations.
The purpose of the SMART VP prize challenge is to create a comprehensive experiential visualization solution (a “visualization platform”) for the subsurface environment that can be readily accessible by experts and non experts — i.e., by scientists, engineers, subsurface operators, and by other decision makers such as landowners.
The U.S. Department of Energy’s (DOE’s) Office of Fossil Energy (FE) “Science-informed Machine Learning to Accelerate Real Time Decisions in the Subsurface” (SMART) Visualization Platform (VP) Challenge prize competition aims to develop an intuitive data visualization tool for the subsurface environment that can be readily accessible by scientists, engineers, subsurface operators, and decision makers. The tool should work in unison with data generated by the SMART Initiatives machine learning solutions to resolve static and dynamic subsurface properties, features, and processes at scales ranging from sub-meters to hundreds of kilometers.
Competitors are asked to focus on bringing the subsurface to life through the development of an innovative, user-friendly, intuitive and attractive visualization platform. We are seeking competitors with software development expertise who are up to the challenge of creating a new visualization platform which will assist in making subsurface insights accessible to a wider range of users and stakeholders.
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The SMART VP Prize Challenge offers up to $1.5 million in total cash prizes. This software development prize challenge is designed around the following two-phased approach:
- Phase 1 – 4 Months – Expected to award up to five prizes and up to a total of $600,000. Competitors will design an independent partial development/prototype visualization solution for subsurface applications using supplied data sets and complete a minimum set of feature requirements.
- Phase 2 – 10 Months – Expected to award one prize of up to $900,000. Phase 1 prize winners will work in collaboration with our SMART Initiative team of scientists and engineers through in-person workshops and online forums to develop a fully operational visual platform of the subsurface.