Announcement Detail

Thursday, March 12, 2026

3pm EDT

Join via Zoom: https://us06web.zoom.us/j/92756548524?pwd=cTFoRXIvNVN4dVFoaHEzK0pQQjhldz09

USACM UQ Virtual Seminar

Scientific Machine Learning Enhanced Forecasting and Uncertainty Quantification in Subsurface Flows

Speaker

Hannah Lu, The University of Texas at Austin

Abstract

Accurate forecasting of subsurface flow migration and trapping remains a central challenge in geoscience. Despite well-established physical understanding and advanced numerical simulators, predictive uncertainty persists due to geological heterogeneity, data scarcity, and the prohibitive cost of high-fidelity simulations. This talk presents a scientific machine learning framework to enhance forecasting and uncertainty quantification capabilities for subsurface flows across scales. Starting from lab-scale experiments, we develop digital twins that combine experimental data, physics-based models, and machine learning surrogates to reproduce and predict multiphase flow dynamics under both pressure-driven and capillary–buoyancy–dominated flow regimes. The framework enables quantitative studies of energy storage retention and flow transport, while identifying key geological and barrier properties that control storage/operation performance. Through collaborations, this research aims to establish a scalable and interpretable SciML foundation for digital twins of subsurface energy storage, bridging experimental observables with predictive modeling of subsurface flow in realistic heterogeneous formations.

Upcoming webinar speakers:

Thursday, April 16: Jinlong Wu, University of Wisconsin-Madison