Integrated Machine Learning and Numerical Modeling for Multiscale Analyses of Coupled Processes in Geosystems

Abstract

Understanding the formation and evolution of geosystems as a result of coupled processes from microscale structure to core-scale samples is critically important for predicting meso- to macro-scale multi-physical behavior, thus providing effective control of subsurface energy recovery and storage activities. In this paper, we present integrated machine learning and numerical modeling approaches that we have developed to understand fundamental geosystem behavior, to predict their mesoscale behavior, and to analyze large-scale performance of such geosystems subject to multi-physical couplings. First of all, we will present a high-level summary of our developed modelling capabilities based on the numerical manifold method [1]. Then we will present theoretical comparison between machine learning and numerical modeling [2]. Using current machine learning techniques on image recognition, we make flexible use of these techniques to integrate experimental data with numerical modeling. By using a convolutional neural network, U-net, we show results of fractures recognized from rock images [3]. By using a machine learning approach referred to as neural style transfer, we will show automatic and efficient mesh generation and optimization from rock images [2]. With a microscale model for capturing dynamic contacts between geomaterials with arbitrary shapes of grains and interfaces [4], we extend this model by accounting for hydro-mechanical couplings and realistic geometry using digital representations of rocks [1]. Using different examples, we show the unique capabilities to simulate coupled processes in fractures at different scales and microscale granular systems, while applying differing physical laws, coupling priorities, and solutions for evolving geometry at multiple scales [5]. We will conclude with perspectives on solutions to bridge the gaps between fundamental and applied geosciences by integrating machine learning and numerical modeling.