Nov. 21, 2024


Description

Abstract: Extensive monitoring, along with data assimilation based on these measurements, will be essential in the management of gigaton-scale geological CO2 storage operations. In this talk, I will present two data assimilation frameworks for uncertainty reduction in flow and geomechanical responses. First, I will describe the use of a deep-learning-based surrogate for model-based data assimilation. A recurrent-residual U-Net, combined with a hierarchical MCMC method, is applied to predict pressure and CO2 saturation in 3D models. Next, I will present a complementary framework, data-space inversion (DSI), to treat more challenging cases involving coupled flow-geomechanics with multiple uncertainty sources. An adversarial autoencoder (AAE) parameterization is developed for the dimension reduction of spatio-temporal data. The AAE is used in DSI posterior sampling to enable direct forecasting of quantities of interest. In this case, DSI is successfully applied to faulted, 3D heterogeneous systems to predict pressure, saturation, stress components, and key aquifer properties.


Featured Speakers

Speaker: Dr. Su Jiang
Speaker Dr. Su Jiang

Bio: Su Jiang is currently a Postdoctoral Fellow at Lawrence Berkeley National Laboratory. She received her PhD in Energy Resources Engineering at Stanford University in 2022 and her BS degree in environmental engineering from Tsinghua University in 2016. Her research interests lie in the interdisciplinary study of subsurface energy development, …

Bio: Su Jiang is currently a Postdoctoral Fellow at Lawrence Berkeley National Laboratory. She received her PhD in Energy Resources Engineering at Stanford University in 2022 and her BS degree in environmental engineering from Tsinghua University in 2016. Her research interests lie in the interdisciplinary study of subsurface energy development, hydrogeology, data assimilation, and scientific machine learning.

Full Description



Organizer

Prithvi Singh Chauhan

Speaker Files


Date and Time

Thu, Nov. 21, 2024

noon - 1 p.m.
(GMT-0500) US/Central

Pricing

  • Virtual
    $5.00
    (ends 11/16/2024)
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Location

Virtual



Group(s): Data Analytics