Predicting the petrophysical properties of rock samples using micro-CT images has gained significant attention recently. However, an accurate and an efficient numerical tool is still lacking. After investigating three numerical techniques, (i) pore network modeling (PNM), (ii) the finite volume method (FVM), and (iii) the lattice Boltzmann method (LBM), a workflow based on machine learning is established for fast and accurate prediction of permeability directly from 3D micro-CT images. We use more than 1100 samples scanned at high resolution and extract the relevant features from these samples for use in a supervised learning algorithm. The approach takes advantage of the efficient computation provided by PNM and the accuracy of the LBM to quickly and accurately estimate rock permeability. The relevant features derived from PNM and image analysis are fed into a supervised machine learning model and a deep neural network to compute the permeability in an end-to-end regression scheme. Within a supervised learning framework, machine and deep learning algorithms based on linear regression, gradient boosting, and physics-informed convolutional neural networks (CNNs) are applied to predict the petrophysical properties of porous rock from 3D micro-CT images. We have performed the sensitivity analysis on the feature importance, hyperparameters, and different learning algorithms to make a prediction. Values of R² scores up to 88% and 91% are achieved using machine learning regression models and the deep learning approach, respectively. Remarkably, a significant gain in computation time—approximately 3 orders of magnitude—is achieved by applied machine learning compared with the LBM. Finally, the study highlights the critical role played by feature engineering in predicting petrophysical properties using deep learning.

中文翻译：

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从网络建模到直接仿真的预测多孔介质渗透率的深度学习视角

最近，使用微CT图像预测岩石样品的岩石物理特性已引起广泛关注。但是，仍然缺少准确而有效的数值工具。在研究了（i）孔隙网络建模（PNM），（ii）有限体积法（FVM）和（iii）晶格玻尔兹曼法（LBM）三种数值技术之后，建立了基于机器学习的工作流程，以实现直接从3D微型CT图像准确预测渗透率。我们使用了1100多个以高分辨率扫描的样本，并从这些样本中提取了相关特征以用于监督学习算法。该方法利用了PNM提供的高效计算和LBM的准确性来快速而准确地估算岩石渗透率。从PNM和图像分析中获得的相关特征被输入到监督的机器学习模型和深度神经网络中，以端到端回归方案计算渗透率。在有监督的学习框架内，基于线性回归，梯度提升和物理学知悉的卷积神经网络（CNN）的机器和深度学习算法可用于从3D微型CT图像预测多孔岩石的岩石物性。我们已经对特征重要性，超参数和不同的学习算法进行了敏感性分析，以做出预测。的价值 基于线性回归，梯度提升和物理信息化卷积神经网络（CNN）的机器和深度学习算法可用于从3D微型CT图像预测多孔岩石的岩石物性。我们已经对特征重要性，超参数和不同的学习算法进行了敏感性分析，以做出预测。的价值 基于线性回归，梯度提升和物理信息化卷积神经网络（CNN）的机器和深度学习算法可用于从3D微型CT图像预测多孔岩石的岩石物性。我们已经对特征重要性，超参数和不同的学习算法进行了敏感性分析，以做出预测。的价值使用机器学习回归模型和深度学习方法分别获得了高达88％和91％的R ²分数。值得注意的是，与LBM相比，通过应用机器学习可以显着提高计算时间（大约3个数量级）。最后，该研究强调了特征工程在使用深度学习预测岩石物理特性中所起的关键作用。