A robust deep learning workflow to predict multiphase flow behavior during geological CO2 sequestration injection and Post-Injection periods

Bicheng Yan*, Bailian Chen, Dylan Robert Harp, Wei Jia, Rajesh J. Pawar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Simulation of multiphase flow in porous media is essential to manage the geologic CO2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO2 as a deep learning feature, leads to the most accurate prediction of CO2 saturation. For the post-injection period, it is key to use cumulative CO2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

Original languageEnglish (US)
Article number127542
JournalJournal of Hydrology
StatePublished - Apr 2022


  • Deep learning
  • Fourier neural operator
  • Geologic sequestration
  • Physics-based simulation

ASJC Scopus subject areas

  • Water Science and Technology


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