A machine learning approach to accelerate convergence of the nonlinear solver in multiphase flow problems is presented here. The approach dynamically controls an acceleration method based on numerical relaxation. It is demonstrated in a Picard iterative solver but is applicable to other types of nonlinear solvers. The aim of the machine learning acceleration is to reduce the computational cost of the nonlinear solver by adjusting to the complexity/physics of the system. Using dimensionless parameters to train and control the machine learning enables the use of a simple two-dimensional layered reservoir for training, while also exploring a wide range of the parameter space. Hence, the training process is simplified and it does not need to be rerun when the machine learning acceleration is applied to other reservoir models. We show that the method can significantly reduce the number of nonlinear iterations without compromising the simulation results, including models that are considerably more complex than the training case.

这里介绍了一种机器学习方法，用于加速非线性求解器在多相流问题中的收敛。该方法动态控制基于数值松弛的加速方法。它在 Picard 迭代求解器中进行了演示，但适用于其他类型的非线性求解器。机器学习加速的目的是通过调整系统的复杂性/物理特性来降低非线性求解器的计算成本。使用无量纲参数训练和控制机器学习可以使用简单的二维分层水库进行训练，同时还可以探索广泛的参数空间。因此，当机器学习加速应用于其他油藏模型时，训练过程被简化并且不需要重新运行。我们表明，该方法可以显着减少非线性迭代次数，而不会影响仿真结果，包括比训练案例复杂得多的模型。