You do not have permission to edit this page, for the following reason:
The action you have requested is limited to users in one of the groups: newuser, fileuploaders, CargoAdmin.
Free text:
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. {{Official URL (simple)|url=https://edx.netl.doe.gov/smart/}} ==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.<ref>{{cite web |url=https://www.netl.doe.gov/node/12781 |title=SMART Phase 3 Launch |publisher=National Energy Technology Laboratory}}</ref> ==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.<ref>{{cite web |url=https://www.energy.gov/fecm/smart-science-informed-machine-learning-accelerating-real-time-decisions-subsurface |title=SMART Overview |publisher=Department of Energy}}</ref> 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.<ref>{{cite web |url=https://edx.netl.doe.gov/smart/ |title=SMART Initiative |publisher=National Energy Technology Laboratory}}</ref> 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.<ref>{{cite web |url=https://www.energy.gov/fecm/articles/department-energy-selects-two-national-lab-led-teams-receive-26-million-advance |title=SMART Funding |publisher=Department of Energy}}</ref> 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.<ref>{{cite web |url=https://www.netl.doe.gov/node/12781 |title=SMART Phase 3 Launch |publisher=National Energy Technology Laboratory}}</ref> 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 ==References== [[Category:Programs and initiatives]] [[Category:Programs]] [[Category:Department of Energy]]