Journal
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
Volume 120, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijggc.2022.103765
Keywords
Data assimilation; Deep learning; InSAR data; Reservoir pressure forecast; ES-MDA
Categories
Funding
- U.S. Department of Energy (DOE) [DE-AC52-07NA2 7344]
- U.S. DOE Office of Fossil Energy
- Total-Energies
- U.S. Department of Energy, Office of Science, Exa-scale Computing Project
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This study presents a fast forecasting method for the reservoir pressure distribution in geologic carbon storage (GCS) using low-cost InSAR data and a deep learning-accelerated workflow. By assimilating surface displacement maps and predicting dynamic reservoir pressure, the method is able to complete data assimilation and pressure forecasting in half an hour.
Fast forecasting of the reservoir pressure distribution during 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 few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic prop-erties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure fore-casting in half an hour on a personal computer.
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