# Artificial Neural Network Surrogate Modeling of Oil Reservoir: a Case Study

## TL;DR

This paper presents an ANN-based surrogate model for oil reservoir simulation that accurately reproduces key parameters with significantly improved computational efficiency, offering a promising tool for reservoir management.

## Contribution

The study introduces a novel ANN surrogate model trained on conventional reservoir data, achieving high accuracy and faster computations compared to traditional methods.

## Key findings

- ANN model captures all output parameters with high accuracy
- Model demonstrates much higher computational performance
- Potential for further research in reservoir modeling

## Abstract

We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to build a predictive model. The ANN-based model allows to reproduce the time dependence of fluids and pressure distribution within the computational cells of the reservoir model. We compare the performance of the ANN-based model with conventional reservoir modeling and illustrate that ANN-based model (1) is able to capture all the output parameters of the conventional model with very high accuracy and (2) demonstrate much higher computational performance. We finally elaborate on further options for research and developments within the area of reservoir modeling.