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  • Subsurface Energy and Environmental Systems (SEES) Lab


  • General Research Areas


  • Subsurface Energy, Water, and Environmental ApplicationsApplication

  • Recent SEES Group Meeting

  • Complex High-Dimensional Geological Systems Effective description

  • Sparse Representations for Dimensionality Reduction
    Effective description

  • Inverse Modeling for Dynamic Data IntegrationKalman Filter

  • Data Science and Machine Learning for Subsurface Flow¬†

  • Model Predictive Control and Optimization for Field Operation and Management

  • Recent SEES Alumni

The Subsurface Energy and Environmental Systems (SEES) lab at USC focuses on integrating advanced system-theoretical principles and machine learning algorithms with insight from modeling and prediction of multiphase fluid flow and transport processes in geologic formations to develop scientifically rigorous and practical methods for efficient recovery and utilization of the subsurface energy, water, and environmental resources.

Our research transcends traditional disciplinary boundaries across Applied Mathematics, Geosciences, Hydrogeology, Petroleum Engineering, and Systems and Control Engineering to develop innovative solutions to challenging problems that are encountered in effectively harnessing subsurface resources while mitigating the impacts of their development and use. At the Viterbi School of Engineering, we are affiliated with the Mork Family Department of Chemical Engineering and Materials Science, Ming Hsieh Departmental of Electrical Engineering, and Sonny Astani Departmental of Civil and Environmental Engineering.

A current research focus in our lab is integrating state-of-the-art data science and machine learning techniques with physical insights from subsurface flow modeling to develop efficient fit-for-purpose predictive tools for subsurface energy and environmental systems.