Pore-GNN: A graph neural network-based framework for predicting flow properties of porous media from micro-CT images

IF 9 1区 地球科学 Q1 ENERGY & FUELS
Mohammed K. Alzahrani, Artur Shapoval, Zhixi Chen, Sheikh S. Rahman
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引用次数: 0

Abstract

This paper presents a hybrid deep learning framework that combines graph neural networks with convolutional neural networks to predict porous media properties. This approach capitalizes on the capabilities of pre-trained convolutional neural networks to extract n-dimensional feature vectors from processed three dimensional micro computed tomography porous media images obtained from seven different sandstone rock samples. Subsequently, two strategies for embedding the computed feature vectors into graphs were explored: extracting a single feature vector per sample (image) and treating each sample as a node in the training graph, and representing each sample as a graph by extracting a fixed number of feature vectors, which form the nodes of each training graph. Various types of graph convolutional layers were examined to evaluate the capabilities and limitations of spectral and spatial approaches. The dataset was divided into 70/20/10 for training, validation, and testing. The models were trained to predict the absolute permeability of porous media. Notably, the proposed architectures further reduce the selected objective loss function to values below 35 mD, with improvements in the coefficient of determination reaching 9%. Moreover, the generalizability of the networks was evaluated by testing their performance on unseen sandstone and carbonate rock samples that were not encountered during training. Finally, a sensitivity analysis is conducted to investigate the influence of various hyperparameters on the performance of the models. The findings highlight the potential of graph neural networks as promising deep learning-based alternatives for characterizing porous media properties. The proposed architectures efficiently predict the permeability, which is more than 500 times faster than that of numerical solvers. Document Type: Original article Cited as: Alzahrani, M. K., Shapoval, A., Chen, Z., Rahman, S. S. Pore-GNN: A graph neural network-based framework for predicting flow properties of porous media from micro-CT images. Advances in Geo-Energy Research, 2023, 10(1):39-55. https://doi.org/10.46690/ager.2023.10.05
Pore-GNN:一种基于图神经网络的框架,用于从微ct图像预测多孔介质的流动特性
本文提出了一种混合深度学习框架,该框架结合了图神经网络和卷积神经网络来预测多孔介质的性质。该方法利用预训练卷积神经网络的能力,从处理过的三维微观计算机断层扫描多孔介质图像中提取n维特征向量,这些图像来自7种不同的砂岩样品。随后,探讨了将计算得到的特征向量嵌入图中的两种策略:一是每个样本(图像)提取单个特征向量,将每个样本作为训练图中的一个节点;二是通过提取固定数量的特征向量,将每个样本表示为一个图,这些特征向量构成每个训练图的节点。研究了各种类型的图卷积层,以评估光谱和空间方法的能力和局限性。数据集被分成70/20/10进行训练、验证和测试。这些模型经过训练可以预测多孔介质的绝对渗透率。值得注意的是,所提出的架构进一步将选定的目标损失函数降低到35 mD以下的值,确定系数提高到9%。此外,通过测试网络在训练过程中未遇到的看不见的砂岩和碳酸盐岩样本上的性能,评估了网络的泛化性。最后,进行了灵敏度分析,探讨了各种超参数对模型性能的影响。这些发现突出了图神经网络作为表征多孔介质特性的有前途的基于深度学习的替代方案的潜力。所提出的结构有效地预测渗透率,比数值求解快500倍以上。Alzahrani, M. K, Shapoval, A., Chen, Z., Rahman, S. S.孔隙- gnn:基于图神经网络的微ct图像多孔介质流动特性预测框架。地球能源研究进展,2023,10(1):39-55。https://doi.org/10.46690/ager.2023.10.05
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来源期刊
Advances in Geo-Energy Research
Advances in Geo-Energy Research natural geo-energy (oil, gas, coal geothermal, and gas hydrate)-Geotechnical Engineering and Engineering Geology
CiteScore
12.30
自引率
8.50%
发文量
63
审稿时长
2~3 weeks
期刊介绍: Advances in Geo-Energy Research is an interdisciplinary and international periodical committed to fostering interaction and multidisciplinary collaboration among scientific communities worldwide, spanning both industry and academia. Our journal serves as a platform for researchers actively engaged in the diverse fields of geo-energy systems, providing an academic medium for the exchange of knowledge and ideas. Join us in advancing the frontiers of geo-energy research through collaboration and shared expertise.
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