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

Forschungsartikel (Zeitschrift)

Zusammenfassung

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.

Details zur Publikation

Veröffentlichungsjahr: 2022
Sprache, in der die Publikation verfasst ist: Englisch
Link zum Volltext: https://link.springer.com/article/10.1007/s10444-022-09981-z