Deep Neural Networks Predicting Oil Movement in a Development Unit
Abstract
We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it "Metamodel") is based on a projection of the system dynamics into a latent variable space, using Variational Autoencoder model, where Recurrent Neural Network predicts the dynamics. We show that being trained on multiple results of the conventional reservoir modelling, the Metamodel does not compromise the accuracy of the reservoir dynamics reconstruction in a significant way. It allows forecasting not only the flow rates from the wells, but also the dynamics of pressure and fluid saturations within the reservoir. The results open a new perspective in the optimization of oilfield development as the scenario screening could be accelerated sufficiently.
Cite
@article{arxiv.1901.02549,
title = {Deep Neural Networks Predicting Oil Movement in a Development Unit},
author = {Pavel Temirchev and Maxim Simonov and Ruslan Kostoev and Evgeny Burnaev and Ivan Oseledets and Alexey Akhmetov and Andrey Margarit and Alexander Sitnikov and Dmitry Koroteev},
journal= {arXiv preprint arXiv:1901.02549},
year = {2019}
}