mlsanityData to 7 Oct 2026

Deep-learning-based coupled flow-geomechanics surrogate model for CO2 sequestration

arXiv 2105.01334 ↗First posted 4 May 2021cs.LGcited by 7

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Abstract (arXiv)

A deep-learning-based surrogate model capable of predicting flow and geomechanical responses in CO2 storage operations is presented and applied. The 3D recurrent R-U-Net model combines deep convolutional and recurrent neural networks to capture the spatial distribution and temporal evolution of saturation, pressure and surface displacement fields. The method is trained using high-fidelity simulation results for 2000 storage-aquifer realizations characterized by multi-Gaussian porosity and log-permeability fields. These numerical solutions are expensive because the domain that must be considered for the coupled problem includes not only the storage aquifer but also a surrounding region, overburden and bedrock. The surrogate model is trained to predict the 3D CO2 saturation and pressure fields in the storage aquifer, and 2D displacement maps at the Earth's surface. Detailed comparisons between surrogate model and full-order simulation results for new (test-case) storage-aquifer realizations are presented. The saturation, pressure and surface displacement fields provided by the surrogate model display a high degree of accuracy, both for individual test-case realizations and for ensemble statistics. Finally, the the recurrent R-U-Net surrogate model is applied with a rejection sampling procedure for data assimilation. Although the observations consist of only a small number of surface displacement measurements, significant uncertainty reduction in pressure buildup at the top of the storage aquifer (caprock) is achieved.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 8 of its 48 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

8 of 48 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  5. 2019A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problemscited by 59
  6. 2018Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous mediacited by 56
  7. 20203D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodelscited by 13
  8. 2020Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimatecited by 5

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 1.

7 papers.

  1. 2022Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEscited by 13
  2. 2022Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSARcited by 7
  3. 2023History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterizationcited by 5
  4. 2023Graph Network Surrogate Model for Subsurface Flow Optimizationcited by 4
  5. 2022Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networkscited by 1
  6. 2025Deep operator network for surrogate modeling of poroelasticity with random permeability fieldscited by 1
  7. 2024CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warmingcited by 0