OPTIMIZATION OF NEURAL NETWORKS FOR PREDICTING OIL RECOVERY FACTOR USING QUANTIZATION TECHNIQUES

Abstract

The optimization of computational efficiency in Artificial Neural Network (ANN) models plays a crucial role in enhancing predictions of oil recovery factors in reservoir engineering and Enhanced Oil Recovery (EOR). This study investigates the application of dynamic quantization to improve the efficiency of ANN models deployed in resource-constrained environments. Dynamic quantization, which converts model weights and activations to lower precision formats during inference, aims to reduce memory usage and accelerate computation without significant loss of predictive accuracy.

Using a synthetic dataset generated from the Buckley-Leverett model, encompassing parameters such as porosity, oil viscosity, permeability, classification, and time series data, we evaluated the impact of dynamic quantization on model size, inference time, and predictive performance. Experimental results demonstrate that dynamic quantization effectively reduces model size and speeds up inference, making it suitable for deployment on edge devices with limited computational resources.

This research contributes to advancing the practical implementation of dynamic quantization techniques in optimizing ANN models for complex predictive tasks in reservoir engineering and related fields. The findings underscore the potential of dynamic quantization in improving computational efficiency and facilitating the deployment of ANN models in real-world applications.