Event Details

Location: CR#1135 Conference Room ISI-MDR

Speaker: Birendra Jha, USC

Abstract: Fluid flow in subsurface reservoirs—relevant to enhanced oil recovery, groundwater remediation, waste disposal, and CO₂ sequestration—is governed by complex hydrodynamic and geomechanical interactions. Viscosity and density contrasts between fluids cause viscous fingering, affecting flow, mixing, and reactions, while fluid injection can induce stress, deformation, and even earthquakes, leading to leakage and reduced storage efficiency. These processes lower recovery rates, limit CO₂ capacity, and increase costs. Physics-based simulations of such coupled systems remain computationally expensive. To address this, we developed deep learning surrogate models to predict viscous fingering, CO₂ leakage, and ground deformation using architectures such as the Fourier Neural Operator (FNO), DeepONet, Vision Transformer (ViT), and U-Net. Trained on physics-based simulation data, these models aim to rapidly and accurately extrapolate to new initial and boundary conditions and rock–fluid properties where traditional simulators are too slow or unreliable.

Virtual Link

Webinar Passcode: 152680

Webinar ID: 935 7017 8243

Speaker Bio

Birendra Jha is an associate professor at USC in Chemical Engineering and Materials Science and Civil and Environmental Engineering. He holds a master’s degree in petroleum engineering from Stanford University and a PhD in civil and environmental engineering from MIT. Before academia, he worked at Schlumberger, Occidental Petroleum, and iReservoir. His research focuses on geomechanics and multiphase flow for CO2 sequestration, geothermal energy, and hydrogen generation and storage. Birendra has received several awards, including the NSF CAREER Award and the SPE Regional Reservoir Description and Dynamics and Formation Evaluation Awards.

For more information on the AI Seminar series please visit ISI AI.

Host: Eric Boxer

POC: Justina Gilleland