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.

中文翻译：

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人工神经网络在储层流动模拟中确定地球化学平衡

在沉积层中注入流体可能会在流体和岩石矿物（例如CO _2）之间产生地球化学相互作用储存在枯竭的水库或盐水层中。为了模拟这种反应性转移过程，将地球化学方程式（平衡方程和动力学方程式）与多孔介质中的组成流耦合在一起，以表示例如降水/溶解现象。提出的分离方法的目的在于用一种替代方法代替地球化学平衡求解器，从而绕过平衡地球化学系统所需的大量消耗时间，同时又保持了精确的平衡计算。本文着重于使用人工神经网络（ANN）确定地球化学平衡，而不是求解地球化学方程组。为了说明拟议的工作流程，提出了地质构造中CO ₂储存的3D案例研究。