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Found 13 papers, 0 papers with code
Graph Network Surrogate Model for Subsurface Flow Optimization
Results are presented for a large set of test cases, in which five injection wells and five production wells are placed randomly throughout the model, with a random control variable (bottom-hole pressure) assigned to each well.
History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization
In this study, we develop and implement (in DSI) a deep-learning-based parameterization to represent spatio-temporal pressure and CO2 saturation fields at a set of time steps.
Surrogate Model for Geological CO2 Storage and Its Use in Hierarchical MCMC History Matching
The trained surrogate model is shown to provide accurate predictions for new realizations over the full range of geological scenarios, with median relative error of 1. 3% in pressure and 4. 5% in saturation.
Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning
In both cases, four assets with different well counts, well configurations, and geostatistical descriptions are considered.
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models
The multifidelity surrogate is also applied for history matching using an ensemble-based procedure, where accuracy relative to reference results is again demonstrated.
Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology
The DRL-based methodology is shown to result in an NPV increase of 15% (for the 2D cases) and 33% (3D cases) relative to robust optimization over prior models, and to an average improvement of 4% in NPV relative to traditional CLRM.
Convolutional-Recurrent Neural Network Proxy for Robust Optimization and Closed-Loop Reservoir Management
The CNN-RNN proxy is trained using simulation results for 300 different sets of BHP schedules and permeability realizations.
Use of low-fidelity models with machine-learning error correction for well placement optimization
In this work, we present an optimization framework in which these simulations are performed with low-fidelity (LF) models.
Deep-learning-based coupled flow-geomechanics surrogate model for CO$_2$ sequestration
The surrogate model is trained to predict the 3D CO2 saturation and pressure fields in the storage aquifer, and 2D displacement maps at the Earth's surface.
3D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodels
Geological parameterization enables the representation of geomodels in terms of a relatively small set of variables.
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization
Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings.
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
High-fidelity numerical simulation results for the posterior geomodels (generated by the surrogate-based data assimilation procedure) are shown to be in essential agreement with the recurrent R-U-Net predictions.
A Deep-Learning-Based Geological Parameterization for History Matching Complex Models
The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN.
