Science-informed Machine Learning for Subsurface Applications: Difference between revisions

|Purpose=The Science-informed Machine Learning for Subsurface Applications program develops AI and machine learning tools to enhance real-time decision-making for subsurface energy systems like carbon storage and geothermal energy. It aims to integrate physics-based models with data-driven approaches to improve forecasting, monitoring, and management of subsurface resources.

|Historic=false

The '''Science-informed Machine Learning for Subsurface Applications''' (SMART) initiative is a Department of Energy program launched in 2020 under the Office of Fossil Energy and Carbon Management (FECM) to harness artificial intelligence (AI) and machine learning (ML) for real-time subsurface energy management. Led by the National Energy Technology Laboratory (NETL) with partners like Battelle and multiple universities, SMART integrates physics-informed ML (PIML) with field data from DOE-supported sites—e.g., carbon storage pilots—handling over 1 petabyte of data across its phases to advance tools for reservoir forecasting and virtual learning.<ref>{{cite web |url=https://edx.netl.doe.gov/smart/ |title=SMART Initiative |publisher=National Energy Technology Laboratory}}</ref> Phase 2, completed by 2023, emphasized deep learning for 3D spatiotemporal models, with Phase 3 (2024 onward) focusing on uncertainty quantification, building on over 20 years of DOE subsurface research.