Announcement Detail
Monday, March 18, 2024
10:00 AM CST
Energy & Earth Systems TTA Webinar
Data assimilation and optimization frameworks for CO2 storage operations
Speaker: Louis J. Durlofsky, Stanford University
Abstract
There are many challenges associated with achieving carbon storage at gigatonne scales. In this talk, I will present some of our recent developments in two areas relevant for the reservoir engineering of CCUS projects. A deep-learning-based surrogate model for data assimilation/history matching will be presented. This surrogate model, which involves an extension of a previously developed recurrent-residual U-Net architecture, is extended to treat coupled flow-geomechanics problems. Its ability to model pressure and CO2 plume locations in new aquifer realizations, and displacement at the Earth’s surface, will be demonstrated. The surrogate model is then applied for MCMC-based history matching. Next, I will describe aframework for optimizing CO2 storage operations using derivative-free algorithms. Different objective functions (involving minimization of mobile CO2 and maximization of storage efficiency), along with a range of practical constraints, are treated. A multifidelity optimization approach will be shown to be effective and to provide improved computational efficiency.
Biography
Louis J. Durlofsky is the Otto N. Miller Professor of Earth Sciences in the Department of Energy Science and Engineering at Stanford University. He codirects the Stanford Smart Fields Consortium and the Stanford Center for Carbon Storage. Earlier in his career, Durlofsky was affiliated with Chevron Energy Technology Company. He holds a BS degree from Penn State, and MS and PhD degrees from MIT, all in chemical engineering. His research interests include subsurface flow simulation and optimization, history matching, uncertainty quantification, and deep-learning-based surrogate modeling.
Upcoming webinar speakers:
Mon. April 15: Prof. Joris Degroote
Mon. May 13: Dr. Alejandro Mota, Sandia National Laboratories