Science-informed Machine Learning for Subsurface Applications

Stored: Science-informed Machine Learning for Subsurface Applications

Science-informed Machine Learning for Subsurface Applications

Type	Program
Sponsor Organization	Office of Fossil Energy and Carbon Management
Top Organization	Department of Energy
Creation Legislation	None
Website	Website
Purpose	SMART, run by devs via AI & ML tools, enhances real-time decisions for subsurface energy like carbon storage & geothermal, integrating physics-based models with data-driven methods to boost forecasting, monitoring, & management.
Program Start	2020
Initial Funding	$10 million
Duration	Ongoing
Historic	No

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.

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.

Official Site

Goals

• Develop real-time forecasting tools to optimize subsurface operational decisions.
• Create virtual learning environments for improved field development and monitoring.
• Enhance subsurface science with scalable, interpretable AI/ML models.

Organization

SMART is managed by FECM through NETL, with Srikanta Mishra (Battelle) as Technical Lead overseeing a consortium of national labs (e.g., LANL, PNNL), universities (e.g., Penn State), and industry partners. Funding from FECM supports cross-disciplinary teams, leveraging NETL’s Energy Data eXchange (EDX) for data curation and high-performance computing resources like Joule for ML development.

History

SMART began in October 2020, driven by the need for faster subsurface decision-making amid growing data from DOE’s carbon sequestration and fossil energy projects. Phase 1 (2020-2022) built foundational ML models using historical data, Phase 2 (2022-2023) advanced deep learning for real-time applications, and Phase 3 (2024-2025) targets uncertainty and scalability per NETL’s January 31, 2024, update. It continues to evolve, supporting DOE’s Energy Earthshots with plans for broader subsurface applications.

Funding

SMART launched with $10 million in 2020 from FECM, with subsequent phases funded through FECM’s R&D budget—e.g., $13 million in FY 2023 for Phase 2. Ongoing support, including the 2024 Phase 3 kickoff, totals over $26 million across teams, funding data processing, tool development, and partnerships with no set end date.

Implementation

SMART deploys its tools via EDX, using PIML techniques like physics-informed neural networks (PINNs) to process petabyte-scale data from field labs. It progresses in phases: Phase 1 curated data, Phase 2 deployed convolutional neural networks for forecasting (web:2), and Phase 3 enhances uncertainty tools, with ongoing adaptation to subsurface challenges like carbon storage and geothermal systems.

Related

• Energy Data eXchange
• Feedstock-Conversion Interface Consortium

External links

• https://edx.netl.doe.gov/smart/
• https://www.energy.gov/fecm/smart-science-informed-machine-learning-accelerating-real-time-decisions-subsurface
• wikipedia:Machine learning

Social media

• https://twitter.com/NETL_DOE
• https://www.linkedin.com/company/netl-doe