Deep learning for pore-scale two-phase flow:Modelling drainage in realistic porous media  

作　　者：ASADOLAHPOUR Seyed Reza JIANG Zeyun LEWIS Helen MIN Chao 

机构地区：[1]Institute of GeoEnergy Engineering,School of Energy,Geoscience,Infrastructure and Society,Heriot-Watt University,Edinburgh,EH144AS,UK [2]School of Science,Southwest Petroleum University,Chengdu 610500,China [3]National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu 610500,China

出　　处：《Petroleum Exploration and Development》2024年第5期1301-1315,共15页石油勘探与开发（英文版）

基　　金：supported by the International Cooperation Programme of Chengdu City(No.2020-GH02-00023-HZ)。

摘　　要：This paper introduces a deep learning workflow to predict phase distributions within complex geometries during two-phase capillary-dominated drainage.We utilize subsamples from Computerized Tomography(CT)images of rocks and incorporate pixel size,interfacial tension,contact angle,and pressure as inputs.First,an efficient morphology-based simulator creates a diverse dataset of phase distributions.Then,two commonly used convolutional and recurrent neural networks are explored and their deficiencies are highlighted,particularly in capturing phase connectivity.Subsequently,we develop a Higher-Dimensional Vision Transformer(HD-ViT)that drains pores solely based on their size,with phase connectivity enforced as a post-processing step.This enables inference for images of varying sizes,resolutions,and inlet-outlet setup.After training on a massive dataset of over 9.5 million instances,HD-ViT achieves excellent performance.We demonstrate the accuracy and speed advantage of the model on new and larger sandstone and carbonate images.We further evaluate HD-ViT against experimental fluid distribution images and the corresponding Lattice-Boltzmann simulations,producing similar outcomes in a matter of seconds.In the end,we train and validate a 3D version of the model.

关 键 词：deep shale gas zipper fracturing finite-discrete element natural fracture zone fracture propagation and intersection law 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TE311[自动化与计算机技术—控制科学与工程]