The factors that affect carbonate dissolution are complex, and it is crucial to acquire efficient and accurate knowledge of carbonate dissolution characteristics for CO capture and storage (CCS) projects. In current research, the most precise outcomes can be achieved through experimental or simulation techniques, but they are frequently computationally demanding and time-consuming. Data-driven machine learning methods can efficiently perform regression prediction tasks. In this paper, we propose a multi-scale hierarchical regression model for the dissolution problem based on residual neural networks, incorporating prior knowledge. We have developed new equations for carbonate rock dissolution and have incorporated the factors that influence this process into 3D image data by introducing Dissolution Degree () parameters. This allows the image data to include both the pore space structure and dissolution information with physical meaning. We utilize the pore phase and matrix phase grayscale thresholds (, ) to eliminate voxel noise in the characteristic maps obtained from the model. This ensures that the predicted characteristics of carbonate rock dissolution are consistent with practical physics knowledge. We selected a total of 5 core samples to test the model. Among them, three samples are from the test set, and the additional two core samples selected have strong and weak correlations with the training set samples, respectively. The predictions were evaluated using semantic segmentation evaluation parameters, porosity, geometrical and topological structure parameters, Péclet and Damköhler numbers, porous media flow field simulations, and absolute permeability. The results of both visual comparisons and quantitative analyses demonstrated a high degree of consistency between predicted and experimental results, and the trained multi-scale hierarchical regression residual neural network with prior knowledge (MSR-Net) demonstrates good accuracy and generalization ability. The results of this study demonstrate that model based on MSR-Net can be utilized to predict the dissolution properties of the pore space in the formation where the core was extracted, along with the related formation.

中文翻译：

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使用先验知识的多尺度残差神经网络预测碳酸盐岩溶解

影响碳酸盐溶解的因素很复杂，有效、准确地了解碳酸盐溶解特征对于二氧化碳捕集和封存（CCS）项目至关重要。在当前的研究中，最精确的结果可以通过实验或模拟技术来实现，但它们通常计算量要求高且耗时。数据驱动的机器学习方法可以有效地执行回归预测任务。在本文中，我们提出了一种基于残差神经网络并结合先验知识的溶解问题的多尺度层次回归模型。我们开发了碳酸盐岩溶解的新方程，并通过引入溶解度 () 参数，将影响该过程的因素纳入 3D 图像数据中。这使得图像数据既包含孔隙空间结构又包含具有物理意义的溶解信息。我们利用孔隙相和基质相灰度阈值 (, ) 来消除从模型获得的特征图中的体素噪声。这确保了碳酸盐岩溶解的预测特征与实际物理知识一致。我们一共选取了5个核心样本来测试模型。其中，3个样本来自测试集，另外选取的2个核心样本分别与训练集样本有强相关性和弱相关性。使用语义分割评估参数、孔隙率、几何和拓扑结构参数、Péclet 和 Damköhler 数、多孔介质流场模拟和绝对渗透率来评估预测。视觉比较和定量分析的结果表明预测结果与实验结果高度一致，并且训练有素的多尺度分层回归残差神经网络（MSR-Net）表现出良好的准确性和泛化能力。本研究的结果表明，基于 MSR-Net 的模型可用于预测提取岩心的地层以及相关地层中孔隙空间的溶解特性。