Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images

X-ray Computed Tomography (CT) imaging is used as a non-destructive strategy for characterizing the internal structure of rocks. One important application of such studies is prediction of the relative permeability of CO2 in reservoirs. Estimation of Carbon Capture and Storage (CCS) has a great impact in mitigation strategies for global warming and controlling the effects of climate change. In this paper, 3D Xray Computed Tomography (CT) image volumes of rocks are characterized for prediction of the CO2 relative permeability. A new analysis pipeline is introduced that extracts high dimensional entropy features from the local 3D voxels. That is followed by a sparse kernelized dimensionality reduction step to alleviate the over-fitting issue. Then, regression analysis is performed using Gaussian Process Regression (GPR). Furthermore, the proposed pipeline is compared with two other deep Neural Networks (NN) models including a Convolutional Neural Network (CNN) regression model as well as a transferred pre-trained ResNet50 model using the rock X-ray training data. Experimental results show improvements in CO2 permeability prediction using the proposed analysis pipeline.