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pro vyhledávání: '"Tang, Hewei"'
Deep learning-based surrogate models have been widely applied in geological carbon storage (GCS) problems to accelerate the prediction of reservoir pressure and CO2 plume migration. Large amounts of data from physics-based numerical simulators are re
Externí odkaz:
http://arxiv.org/abs/2308.09113
Autor:
Tang, Hewei, Fu, Pengcheng, Jo, Honggeun, Jiang, Su, Sherman, Christopher S., Hamon, François, Azzolina, Nicholas A., Morris, Joseph P.
Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from wells, leading to h
Externí odkaz:
http://arxiv.org/abs/2201.08543
Estimating porosity models via seismic data is challenging due to the signal noise and insufficient resolution of seismic data. Although impedance inversion is often used by combining with well logs, several hurdles remain to retrieve sub-seismic sca
Externí odkaz:
http://arxiv.org/abs/2111.13581
Autor:
Tang, Hewei, Fu, Pengcheng, Sherman, Christopher S., Zhang, Jize, Ju, Xin, Hamon, François, Azzolina, Nicholas A., Burton-Kelly, Matthew, Morris, Joseph P.
Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation w
Externí odkaz:
http://arxiv.org/abs/2105.09468
Publikováno v:
In Journal of Hydrology February 2024 629
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