# Deep Neural Networks Predicting Oil Movement in a Development Unit

## TL;DR

This paper introduces a deep learning-based workflow that significantly accelerates 3D reservoir simulation data analysis, enabling efficient forecasting of oil flow, pressure, and saturation dynamics for improved oilfield development planning.

## Contribution

The novel Metamodel combines Variational Autoencoders and Recurrent Neural Networks to efficiently predict reservoir dynamics without compromising accuracy, vastly speeding up traditional simulation methods.

## Key findings

- The Metamodel accurately reconstructs reservoir dynamics.
- It forecasts flow rates, pressure, and saturation effectively.
- The workflow is orders of magnitude faster than conventional methods.

## 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.