no code implementations • 15 Apr 2022 • Nanzhe Wang, Haibin Chang, Xiangzhao Kong, Martin O. Saar, Dongxiao Zhang
In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs.
no code implementations • 17 Aug 2020 • Svetlana Kyas, Diego Volpatto, Martin O. Saar, Allan M. M. Leal
This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in Leal et al. (2020) when applied to different reactive transport problems in heterogeneous porous media.
no code implementations • 16 Aug 2017 • Allan M. M. Leal, Dmitrii A. Kulik, Martin O. Saar
We demonstrate the use of this smart chemical equilibrium method in a reactive transport modeling example and show that, even at early simulation times, the majority of all equilibrium calculations are quickly predicted and, after some time steps, the machine-learning-accelerated chemical solver has been fully trained to rapidly perform all subsequent equilibrium calculations, resulting in speedups of almost two orders of magnitude.