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Found 6 papers, 0 papers with code
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.
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 Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR
We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure.
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.
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic
However, in many cases a reduction in precision comes at a small cost to the accuracy of the resultant network.
