Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks

TL;DR

This paper introduces a GNN-based method that transforms 3D digital rock images into graph datasets using the Mapper algorithm, enabling accurate and memory-efficient prediction of rocks' elastic moduli, outperforming CNNs.

Contribution

The study presents a novel GNN approach combined with Mapper for predicting rock elastic moduli from CT scans, demonstrating improved accuracy and efficiency over traditional CNN methods.

Findings

GNN models outperform CNNs in predicting unseen rock properties.

Graph representations reduce GPU memory requirements significantly.

High prediction accuracy maintained across various subcube sizes.

Abstract

This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images. We use the Mapper algorithm to transform 3D digital rock images into graph datasets, encapsulating essential geometrical information. These graphs, after training, prove effective in predicting elastic moduli. Our GNN model shows robust predictive capabilities across various graph sizes derived from various subcube dimensions. Not only does it perform well on the test dataset, but it also maintains high prediction accuracy for unseen rocks and unexplored subcube sizes. Comparative analysis with Convolutional Neural Networks (CNNs) reveals the superior performance of GNNs in predicting unseen rock properties. Moreover, the graph representation of microstructures significantly reduces GPU memory requirements (compared to the grid representation for CNNs), enabling greater flexibility in the batch size selection. This work demonstrates the potential of GNN models in enhancing the prediction accuracy of rock properties and boosting the efficiency of digital rock analysis.