10 papers
OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI
Ipsita Bhar, Huseyin Tuna Erdinc, Thales Souza +2
The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of traditionally expensive iter…
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco +2
Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Wav…
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…