Conference Paper/Proceeding/Abstract 358 views
Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Swansea University Author:
Sara Sharifzadeh
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DOI (Published version): 10.1109/icmla55696.2022.00081
Abstract
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 impac...
| Published in: | 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) |
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| Published: |
IEEE
2022
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| Online Access: |
http://dx.doi.org/10.1109/icmla55696.2022.00081 |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa63204 |
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| Abstract: |
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. |
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| College: |
Faculty of Science and Engineering |