Adaptive machine learning based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery

Keil T, Kleikamp H, Lorentzen R, Oguntola M, Ohlberger M

Abstract

In this contribution, we develop an efficient surrogate modeling frame- work for simulation-based optimization of enhanced oil recovery, where we particularly focus on polymer flooding. The computational approach is based on an adaptive training procedure of a neural network that directly approximates an input-output map of the underlying PDE- constrained optimization problem. The training process thereby focuses on the construction of an accurate surrogate model solely related to the optimization path of an outer iterative optimization loop. True evalua- tions of the objective function are used to finally obtain certified results. Numerical experiments are given to evaluate the accuracy and efficiency of the approach for a heterogeneous five-spot benchmark problem.

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