Industrial applications of digital rock technology

灵敏度感知岩石物理增强数字孪生用于地下储能监测

Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring

针对地下储能监测中岩石物理模型失配导致预测不可靠的问题，提出上下文感知灵敏度分析增强数字孪生，学习数据、状态与模型间依赖，支持实时假设测试，提升可解释性与决策可靠性。

结合CNN与层次均匀化方法对大型数字岩心图像弹性属性进行均匀化处理

Homogenizing elastic properties of large digital rock images by combining CNN with hierarchical homogenization method

针对大型数字岩心图像均匀化计算成本高的问题，提出结合3D CNN与层次均匀化方法，通过CNN处理小子图像、DNS处理中间图像，实现高效且准确的弹性模量预测，并验证了模型的迁移能力。

RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice from the perspective of video generation

Random reconstruction of three-dimensional (3D) digital rocks from two-dimensional (2D) slices is crucial for elucidating the microstructure of rocks and its effects on pore-scale flow in terms of numerical modeling, since massive samples are usually required to handle intrinsic uncertainties. Despite remarkable advances achieved by traditional process-based methods, statistical approaches and recently famous deep learning-based models, few works have focused on producing several kinds of rocks with one trained model and allowing the reconstructed samples to satisfy certain given properties, s…

用于层级均匀化的卷积神经网络代理：数字岩石弹性模量快速预测

A convolutional neural network surrogate for hierarchical homogenization: fast elastic moduli prediction of digital rocks

提出结合轻量级3D卷积神经网络与层级均匀化方法，通过预测子立方体弹性模量并上尺度化，实现数字岩石有效弹性模量的快速准确预测。

使用图神经网络预测岩石的有效弹性模量

Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks

本研究提出基于图神经网络的方法，利用Mapper算法将3D数字岩石CT图像转为图数据预测有效弹性模量，相比CNN具有更高预测精度且显著降低GPU内存需求。

High accuracy capillary network representation in digital rock reveals permeability scaling functions

Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simul…

通过增强岩石物理建模提升二氧化碳封存监测数字孪生的鲁棒性

Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling

本研究针对地质碳封存（GCS）监测中数字孪生（DS）因假设不准确导致预测可靠性下降的问题，提出用多样化岩石物理模型增强预测集合的方法，有效提升预测精度，尤其在区分饱和度模型方面效果显著。

The LBPM software package for simulating multiphase flow on digital images of porous rocks

Direct pore scale simulations of two-fluid flow on digital rock images provide a promising tool to understand the role of surface wetting phenomena on flow and transport in geologic reservoirs. We present computational protocols that mimic conventional special core analysis laboratory (SCAL) experiments, which are implemented within the open source LBPM software package. Protocols are described to simulate unsteady displacement, steady-state flow at fixed saturation, and to mimic centrifuge experiments. These methods can be used to infer relative permeability and capillary curves, and otherwis…

低应力水平下岩石非线性变形的基于图像的建模

Image-based modelling of rock non-linear deformation under low-stress levels

提出一种基于图像的数字岩石建模新方法，通过将部分体积体素视为包含微裂纹的过渡相并赋予中间弹性模量，利用有限元方法有效模拟了低应力条件下因微裂纹闭合导致的岩石非线性变形。

Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph

We present a SE(3)-equivariant graph neural network (GNN) approach that directly predicting the formation factor and effective permeability from micro-CT images. FFT solvers are established to compute both the formation factor and effective permeability, while the topology and geometry of the pore space are represented by a persistence-based Morse graph. Together, they constitute the database for training, validating, and testing the neural networks. While the graph and Euclidean convolutional approaches both employ neural networks to generate low-dimensional latent space to represent the feat…

基于机器学习的增强型数据同化框架用于多尺度碳酸盐岩表征

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization

本文提出一种基于机器学习的数据同化框架，利用实验排水相对渗透率测量高效表征微尺度结构，通过DNN-ESMDA算法实现多尺度数字岩石建模，显著提升计算效率。