**报告题目：**A glimpse of the researches and applications of deep learning for digital rock physics

**报告人：**蒋泽云（Heriot-Watt大学，教授）

**报告时间：****2**021年6月11日16:00-18:00

**报告地点：**腾讯会议ID：259 474 071

**摘要:**The advent of deep learning (DL) marked a milestone in the real-life applicability, as now very complex problems can be solved with unprecedented accuracy. DL generally require little explicit prior knowledge and are distinctively efficient in extracting complicated patterns. These capabilities turn them into feasible candidates for replacing and/or assisting conventional time-consuming and computationally expensive experiments.

This talk aims to show how the power of deep learning can be harnessed to both estimate porous-media properties and develop new insights. The main objectives are: (1) provide a general overview of how DLs have already been used in terms of single/multi-phase flow; (2) demonstrate the potentials of DL in digital rock physics through case studies; (3) discuss DL-based approaches to explore the physics of the porous media.

First, the relevant body of research is considered so that advancements, gaps and potentials can be identified. Then, an implementation map is laid out, encompassing the simplest to most comprehensive applications. Secondly, several cases are presented to show-case its ability. Thirdly, future research is briefly discussed. It is proposed that to develop reliable multi-phase predictors, large databases must be synthesized by collecting, resampling, augmenting, and grouping images and the corresponding properties. Consequently, deep neural networks can be trained for various rock types and processes. Singular or ensembles of DL networks may either be used to make predictions or to serve as the base to be customized for other applications, i.e., transfer learning. Final models can be put to ultimate real-life testing by comparing against experimental data, e.g., phase distributions from synchrotron imaging. Rather than creating mere black-box estimators, one must strive to understand how the networks extract information and link relationships, by looking at layer architectures, weights and other elements. The goal should be to gain insights into various flow functions and the physics of certain flow behaviours. Furthermore, since trained models are very fast to run, they make perfect assets for such tasks as sensitivity/uncertainty analysis and back-calculation of input features, for instance, to see what wettability distribution can result in a specific flow parameter.

**报告人简介：**蒋泽云教授从2004年以后主要从事孔隙介质（如岩石、土壤等）异质多尺度结构分析和流体渗流模型研究,在Water Resource research, Transport in Porous Media, Fuel等期刊和国际会议上发表论文40多篇，在国内外参与并主导若干重大科研项目。主要从事微观空隙结构分析及网络渗流模型的研究,独立开发软件系统PAT–Pore Analysis Tools（这一软件在学术和工业界得到广泛的使用）。擅长于岩石微观图像几何拓扑属性的分析,并建立多尺度孔隙模型和实施数值模拟,建立其微观结构（如孔隙度、孔尺寸、形状、连通性、孔壁粗糙度等）与宏观流体属性（如渗透率、毛细管压力、性、相对渗透率、电阻率等）间的理论或经验公式。

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**举办单位：**科研处、理学院、人工智能研究院