Science-informed Machine Learning for Accelerating Real-Time Decisions in Carbon Storage Applications

This project is funded by DOE - Office of Fossil Energy, 2020/05 - present.

Today’s technology can securely store captured carbon dioxide deep in the subsurface of the ground, but slow data processing can result in operational inefficiencies. To meet this challenge, the Office of Fossil Energy (FE) of DOE developed a Science-Informed Machine Learning to Accelerate Real Time Decisions (SMART-CS) initiative. Using science-based machine learning and AI, this initiative will enable better reservoir management through more rapid decision making. It will develop real-time visualization, forecasting capabilities, and virtual learning environments. As a result, the SMART-CS initiative will help stakeholders and regulators overcome costly inefficiencies while increasing their confidence that the geologic carbon storage is secure.

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The SMART-CS Initiative aims to transform how people interact with subsurface data, improving the efficiency and effectiveness of field-scale carbon storage by application of science-based machine learning and data analytics.

The SMART team is engaging with university, national lab, and industry partners, and is building off data collected from field laboratories and regional partnerships, that have been part of the Carbon Storage Program during the past 15 years. The SMART team will continue to collaborate with other Carbon Storage Program efforts — especially the Regional Initiatives — to collect and analyze data and share insights. Additionally, SMART-CS will be integrated with SMART-OG, which is focused on research within the Oil and Gas Program to form the overall SMART Initiative, which will transform our understanding of the subsurface through real-time visualization, forecasting, and virtual learning.