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Geochemical equilibrium determination using an artificial neural network in compositional reservoir flow simulation

  • Dominique GuérillotEmail author
  • Jérémie Bruyelle
Open Access
Original Paper
  • 31 Downloads

Abstract

The fluid injection in sedimentary formations may generate geochemical interactions between the fluids and the rock minerals, e.g., CO2 storage in a depleted reservoir or a saline aquifer. To simulate such reactive transfer processes, geochemical equations (equilibrium and kinetics equations) are coupled with compositional flows in porous media in order to represent, for example, precipitation/dissolution phenomena. The aim of the decoupled approach proposed consists in replacing the geochemical equilibrium solver with a substitute method to bypass the huge consuming time required to balance the geochemical system while keeping an accurate equilibrium calculation. This paper focuses on the use of artificial neural networks (ANN) to determine the geochemical equilibrium instead of solving geochemical equations system. To illustrate the proposed workflow, a 3D case study of CO2 storage in geological formation is presented.

Keywords

Reservoir simulation Compositional Heterogeneity CO2 storage Chemically reacting flows Artificial neural network 

Mathematics Subject Classification (2010)

80A32 76S05 68T05 

Notes

Acknowledgments

Open Access funding provided by the Qatar National Library.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Texas A&M University at QatarDohaQatar

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