# Deep Neural Networks Predicting Oil Movement in a Development Unit

arXiv: https://arxiv.org/abs/1901.02549 · HTML: https://mlsanity.com/paper/1901.02549/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models](https://mlsanity.com/paper/2404.17884/) (2024)
- [End-to-end neural network approach to 3D reservoir simulation and adaptation](https://mlsanity.com/paper/2102.10304/) (2021)
- [Estimating oil and gas recovery factors via machine learning: Database-dependent accuracy and reliability](https://mlsanity.com/paper/2210.12491/) (2022)
- [Feature Qualification by Deep Nets: A Constructive Approach](https://mlsanity.com/paper/2503.18676/) (2025)

## Abstract (arXiv)

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.