Name
Title | ||
|---|---|---|
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems |
Abstract | ||
|---|---|---|
•Deep-learning-based surrogate model for dynamic subsurface flow is developed.•Method uses a residual U-net and convolutional LSTM recurrent network.•Surrogate capable of predicting states and well rates in channelized geomodels.•Data assimilation accomplished by combining surrogate with CNN-PCA parameterization.•Accuracy of posterior flow predictions demonstrated by comparison with simulations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.jcp.2020.109456 | Journal of Computational Physics |
Keywords | DocType | Volume |
Surrogate model,Deep-learning,Reservoir simulation,History matching,Inverse modeling | Journal | 413 |
ISSN | Citations | PageRank |
0021-9991 | 3 | 0.42 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tang Meng | 1 | 3 | 0.42 |
| Yimin Liu | 2 | 158 | 25.46 |
| Louis J. Durlofsky | 3 | 58 | 8.64 |