Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images. (1st February 2022)

• Record Type: Journal Article
• Authors: Golsanami, Naser; Jayasuriya, Madusanka N.; Yan, Weichao; Fernando, Shanilka G.; Liu, Xuefeng; Cui, Likai; Zhang, Xuepeng; Yasin, Qamar; Dong, Huaimin; Dong, Xu
• Is Part Of: Energy. Volume 240(2022)
• Publication Date: 2022-02-01
• Subjects: Clay minerals -- Deep learning -- MATLAB App -- Fluid flow -- Lattice-Boltzmann method; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.122599
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The presence of clays in hydrocarbon reservoirs challenges the producible amount of oil and gas significantly. Therefore, this study reports a detailed quantitative characterization of clays' specific properties from two fundamental aspects which include clays' type and amount, and their impact on reservoir's fluid flow. We used Scanning Electron Microscopy (SEM) images and respectively adopted deep learning for typing and quantifying clays, and the Lattice-Boltzmann Method (LBM) for flow simulations with and without the presence of clays. The trained deep learning model of the present study was translated into a MATLAB application that is a convenient tool for clay characterization by the future user. This model was trained using 2160 images of different clay minerals based on transfer learning using AlexNet and resulted in more than 95.4% accuracy while applied on the unforeseen images. Moreover, we established the technique of depth-slicing of 2D SEM images, which provides the possibility of 3D processing of the routine SEM images. The results from this technique proved that clays could reduce reservoir porosity and permeability by more than 30% and 400 mD, respectively. The introduced approach of the present study provides new insights into the detailed impacts of clay minerals on the reservoir's quality. Graphical abstract: Image 1 Highlights: Developing an App for deep learning-based quantification of clay minerals. Developing the depth-slicing technique to address the issue of the "depth of field". Deep learning could satisfyingly type and quantify clay textures in SEM images. 3D processing of SEM images could better reveal impacts of clays on reservoir rock. 3D quantitative analysis of various clays' impact on the pore space remains unknown.