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Abstract

Summary

The application of chemical method for hydrocarbons extraction has attracted increasing interest in the reservoir simulation community. To simulate such reactive transfer processes, compositional flows in porous media with a complex mineralogical must be coupled with the chemical equilibria in the aqueous phase and the precipitation / dissolution reactions of the minerals.

The most important time consumed during reactive transport simulation is the geochemical equilibrium (about 30% to 80%). Typically, chemical equilibria are computed for each cell at each time-step by solving an equations system with the iterative Newton-Raphson method. To reduce the computation time, the number of species in solution is often reduce. However, such assumption leads to a less of accuracy of results.

Instead of simplifying the geochemical model, an approach that mimic the resolution of geochemical equilibrium can be considered. The aim of the approach is to provide a substitute method to bypass the huge consuming time required to balance the chemical system. This paper focuses on the use of artificial neural networks (ANN) to replace the geochemical equilibrium package. It is widely admitted that ANN are the most efficient response surface model due to the no linear behavior of the output again the parameters.

This paper presents a complete workflow for compositional reservoir simulation using an artificial neural network to determine the chemical equilibrium instead of solving equations system. This approach substantially reduces the computation time while keeping an accurate equilibrium calculation.

To illustrate the proposed workflow, a case study of CO2 storage in geological formation is presented. The compositional system involves 11 aqueous species, 1 mineral component, 6 chemical equilibrium reactions and 1 mineral dissolution/precipitation reaction.

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/content/papers/10.3997/2214-4609.201802232
2018-09-03
2024-04-25

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Geochemical Equilibrium Determination Using An Artificial Neural Network In Compositional Reservoir Flow Simulation
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802232
/content/papers/10.3997/2214-4609.201802232
/content/papers/10.3997/2214-4609.201802232
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