Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey
J Zhang - Wiley Interdisciplinary Reviews: Computational …, 2021 - Wiley Online Library
Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …
uncertainties that result from stochastic variations and a lack of knowledge or data in the …
Digital rock physics, chemistry, and biology: challenges and prospects of pore-scale modelling approach
Conventional and unconventional hydrocarbon rocks have complicated pore structures with
heterogeneities distributed over various length scales (from nanometre to centimetre or even …
heterogeneities distributed over various length scales (from nanometre to centimetre or even …
Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence
Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
A general gridding, discretization, and coarsening methodology for modeling flow in porous formations with discrete geological features
M Karimi-Fard, LJ Durlofsky - Advances in water resources, 2016 - Elsevier
A comprehensive framework for modeling flow in porous media containing thin, discrete
features, which could be high-permeability fractures or low-permeability deformation bands …
features, which could be high-permeability fractures or low-permeability deformation bands …
Striving to translate shale physics across ten orders of magnitude: What have we learned?
Shales will play an important role in the successful transition of energy from fossil-based
resources to renewables in the coming decades. Aside from being a significant source of …
resources to renewables in the coming decades. Aside from being a significant source of …
Reduced-order modeling of subsurface multi-phase flow models using deep residual recurrent neural networks
JN Kani, AH Elsheikh - Transport in Porous Media, 2019 - Springer
We present a reduced-order modeling technique for subsurface multi-phase flow problems
building on the recently introduced deep residual recurrent neural network (DR …
building on the recently introduced deep residual recurrent neural network (DR …
Inverse modeling for subsurface flow based on deep learning surrogates and active learning strategies
Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial
to characterize underground geologic properties and reduce prediction uncertainty …
to characterize underground geologic properties and reduce prediction uncertainty …
Machine learning assisted relative permeability upscaling for uncertainty quantification
Traditional two-phase relative permeability upscaling entails the computation of upscaled
relative permeability functions for each coarse block (or interface). The procedure can be …
relative permeability functions for each coarse block (or interface). The procedure can be …
Implementation of physics-based data-driven models with a commercial simulator
The use of full-physics models in close-loop reservoir management can be computationally
prohibitive as a large number of simulation runs are required for history matching and …
prohibitive as a large number of simulation runs are required for history matching and …
A robust proxy for production well placement optimization problems
Finding optimum well locations is still among the most challenging reservoir engineering
problems. Reservoir simulators are routinely used to evaluate different configuration of well …
problems. Reservoir simulators are routinely used to evaluate different configuration of well …