Flow, transport, mechanical, and fracture properties of porous media depend on their morphology and are usually estimated by experimental and/or computational methods. The precision of the computational approaches depends on the accuracy of the model that represents the morphology. If high accuracy is required, the computations and even experiments can be quite time-consuming. At the same time, linking the morphology directly to the permeability, as well as other important flow and transport properties, has been a long-standing problem. In this paper, we develop a new network that utilizes a deep learning (DL) algorithm to link the morphology of porous media to their permeability. The network is neither a purely traditional artificial neural network (ANN), nor is it a purely DL algorithm, but, rather, it is a hybrid of both. The input data include three-dimensional images of sandstones, hundreds of their stochastic realizations generated by a reconstruction method, and synthetic unconsolidated porous media produced by a Boolean method. To develop the network, we first extract important features of the images using a DL algorithm and then feed them to an ANN to estimate the permeabilities. We demonstrate that the network is successfully trained, such that it can develop accurate correlations between the morphology of porous media and their effective permeability. The high accuracy of the network is demonstrated by its predictions for the permeability of a variety of porous media.

中文翻译：

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通过深度学习将多孔介质的形态与其宏观渗透率联系起来

多孔介质的流动、传输、机械和断裂特性取决于它们的形态，通常通过实验和/或计算方法进行估计。计算方法的精度取决于表示形态的模型的准确性。如果需要高精度，计算甚至实验可能会非常耗时。同时，将形态与渗透率以及其他重要的流动和传输特性直接联系起来，一直是一个长期存在的问题。在本文中，我们开发了一种新网络，该网络利用深度学习 (DL) 算法将多孔介质的形态与其渗透率联系起来。该网络既不是纯粹的传统人工神经网络 (ANN)，也不是纯粹的 DL 算法，而是两者的混合。输入数据包括砂岩的 3D 图像，通过重建方法生成的数百个随机实现，以及通过布尔方法生成的合成松散多孔介质。为了开发网络，我们首先使用 DL 算法提取图像的重要特征，然后将它们提供给 ANN 以估计渗透率。我们证明该网络已成功训练，因此它可以在多孔介质的形态与其有效渗透率之间建立准确的相关性。该网络对各种多孔介质渗透率的预测证明了该网络的高精度。为了开发网络，我们首先使用 DL 算法提取图像的重要特征，然后将它们提供给 ANN 以估计渗透率。我们证明该网络已成功训练，因此它可以在多孔介质的形态与其有效渗透率之间建立准确的相关性。该网络对各种多孔介质渗透率的预测证明了该网络的高精度。为了开发网络，我们首先使用 DL 算法提取图像的重要特征，然后将它们提供给 ANN 以估计渗透率。我们证明该网络已成功训练，因此它可以在多孔介质的形态与其有效渗透率之间建立准确的相关性。该网络对各种多孔介质渗透率的预测证明了该网络的高精度。