A review of proxy modeling highlighting applications for reservoir engineering
Numerical models can be used for many purposes in oil and gas engineering, such as
production optimization and forecasting, uncertainty analysis, history matching, and risk …
production optimization and forecasting, uncertainty analysis, history matching, and risk …
Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification
Identification of a groundwater contaminant source simultaneously with the hydraulic
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …
Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies
SM Agar, S Geiger - Geological Society, London, Special …, 2015 - lyellcollection.org
The introduction reviews topics relevant to the fundamental controls on fluid flow in
carbonate reservoirs and to the prediction of reservoir performance. The review provides …
carbonate reservoirs and to the prediction of reservoir performance. The review provides …
Probabilistic model updating via variational Bayesian inference and adaptive Gaussian process modeling
The estimation of the posterior probability distribution of unknown parameters remains a
challenging issue for model updating with uncertainties. Most current studies are based on …
challenging issue for model updating with uncertainties. Most current studies are based on …
Predicting field production rates for waterflooding using a machine learning-based proxy model
Waterflooding, during which water is injected in the reservoir to increase pressure and
therefore boost oil production, is extensively used as a secondary oil recovery technology …
therefore boost oil production, is extensively used as a secondary oil recovery technology …
Deep residual U-net convolution neural networks with autoregressive strategy for fluid flow predictions in large-scale geosystems
Z Jiang, P Tahmasebi, Z Mao - Advances in Water Resources, 2021 - Elsevier
The inherent complexity of the fluid flow in subsurface systems brings potential inevitable
uncertainty in their characterization. Computationally intensive high-dimensional inversion …
uncertainty in their characterization. Computationally intensive high-dimensional inversion …
Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non‐Gaussian hydraulic conductivities
Inverse modeling for the estimation of non‐Gaussian hydraulic conductivity fields in
subsurface flow and solute transport models remains a challenging problem. This is mainly …
subsurface flow and solute transport models remains a challenging problem. This is mainly …
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 …
A novel hybrid recurrent convolutional network for surrogate modeling of history matching and uncertainty quantification
Automatic history matching (AHM) has been widely studied in petroleum engineering due to
it can provide reliable numerical models for reservoir development and management …
it can provide reliable numerical models for reservoir development and management …
Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems
We consider a high-dimensional nonlinear computational model of a dynamical system,
parameterized by a vector-valued control parameter, in the presence of uncertainties …
parameterized by a vector-valued control parameter, in the presence of uncertainties …