Improving deep learning performance for predicting large-scale geological CO2 sequestration modeling through feature coarsening

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

Research output: Contribution to journalArticlepeer-review

Abstract

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.
Original languageEnglish (US)
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Nov 30 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-12-02
Acknowledged KAUST grant number(s): BAS/1/1423-01-01, FCC/1/4491-22-01
Acknowledgements: The authors acknowledge the financial support by US DOE’s Fossil Energy Program Office through the project, Science-informed Machine Learning to Accelerate Real Time (SMART) Decisions in Subsurface Applications. Funding for SMART is managed by the National Energy Technology Laboratory (NETL). Bicheng Yan also thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1423-01-01 and FCC/1/4491-22-01.

ASJC Scopus subject areas

  • General

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