9 papers
A reduced-order derivative-informed neural operator for subsurface fluid-flow
Jeongjin Park, Grant Bruer, Huseyin Tuna Erdinc +2
Neural operators have emerged as cost-effective surrogates for expensive fluid-flow simulators, particularly in computationally intensive tasks such as permeability inversion from…
Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring
Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, Felix J. Herrmann
Underground energy storage, which includes storage of hydrogen, compressed air, and CO2, requires careful monitoring to track potential leakage pathways, a situation where time-lap…
Power-scaled Bayesian Inference with Score-based Generative Models
Huseyin Tuna Erdinc, Yunlin Zeng, Abhinav Prakash Gahlot +1
We propose a score-based generative algorithm for sampling from power-scaled priors and likelihoods within the Bayesian inference framework. Our algorithm enables flexible control…
Well2Flow: Reconstruction of reservoir states from sparse wells using score-based generative models
Shiqin Zeng, Haoyun Li, Abhinav Prakash Gahlot +1
This study investigates the use of score-based generative models for reservoir simulation, with a focus on reconstructing spatially varying permeability and saturation fields in sa…
Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling
Abhinav Prakash Gahlot, Felix J. Herrmann
To meet climate targets, the IPCC underscores the necessity of technologies capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage (GCS) playing a central r…
Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology
Abhinav Prakash Gahlot, Rafael Orozco, Felix J. Herrmann
Geological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely…