Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR
Abstract
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 properties 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 forecasting in half an hour on a personal computer.
- Authors:
-
[1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- TotalEnergies E&P Research and Technology, Houston, TX (United States)
- University of North Dakota, Grand Forks, ND (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 2005124
- Report Number(s):
- LLNL-JRNL-830880
Journal ID: ISSN 1750-5836; 1047530
- Grant/Contract Number:
- AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- International Journal of Greenhouse Gas Control
- Additional Journal Information:
- Journal Volume: 120; Journal ID: ISSN 1750-5836
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 25 ENERGY STORAGE; Data assimilation; Deep learning; InSAR data; Reservoir pressure forecast; ES-MDA
Citation Formats
Tang, Hewei, Fu, Pengcheng, Jo, Honggeun, Jiang, Su, Sherman, Christopher S., Hamon, François, Azzolina, Nicholas A., and Morris, Joseph P. Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR. United States: N. p., 2022.
Web. doi:10.1016/j.ijggc.2022.103765.
Tang, Hewei, Fu, Pengcheng, Jo, Honggeun, Jiang, Su, Sherman, Christopher S., Hamon, François, Azzolina, Nicholas A., & Morris, Joseph P. Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR. United States. https://doi.org/10.1016/j.ijggc.2022.103765
Tang, Hewei, Fu, Pengcheng, Jo, Honggeun, Jiang, Su, Sherman, Christopher S., Hamon, François, Azzolina, Nicholas A., and Morris, Joseph P. Fri .
"Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR". United States. https://doi.org/10.1016/j.ijggc.2022.103765. https://www.osti.gov/servlets/purl/2005124.
@article{osti_2005124,
title = {Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR},
author = {Tang, Hewei and Fu, Pengcheng and Jo, Honggeun and Jiang, Su and Sherman, Christopher S. and Hamon, François and Azzolina, Nicholas A. and Morris, Joseph P.},
abstractNote = {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 properties 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 forecasting in half an hour on a personal computer.},
doi = {10.1016/j.ijggc.2022.103765},
journal = {International Journal of Greenhouse Gas Control},
number = ,
volume = 120,
place = {United States},
year = {Fri Sep 02 00:00:00 EDT 2022},
month = {Fri Sep 02 00:00:00 EDT 2022}
}
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