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Lecture: A glimpse of the researches and applications of deep learning for digital rock physics

Lecturer: Professor Zeyun Jiang (Heriot-Watt University)

Time: June 11^th, 2021, 16:00-18:00

Avenue: Tencent Meeting (ID: 259474071)

Abstract: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 requires little explicit prior knowledge and is 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 behaviors. 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.

Welcome!

Organizer and sponsor:Science and Technology Department

                        School of Sciences

                        Artificial Intelligence Institute