Predicting carbonate rock dissolution using multi-scale residual neural networks with prior knowledge

Abstract

The factors that affect carbonate dissolution are complex, and it is crucial to acquire efficient and accurate knowledge of carbonate dissolution characteristics for CO2 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 (Dd) 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 (Pt, Mt) 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.