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Conference Paper/Proceeding/Abstract 564 views

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

Sara Sharifzadeh Orcid Logo

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

Swansea University Author: Sara Sharifzadeh Orcid Logo

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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...

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Published in: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Published: IEEE 2022
Online Access: http://dx.doi.org/10.1109/icmla55696.2022.00081
URI: https://cronfa.swan.ac.uk/Record/cronfa63204
first_indexed 2023-04-25T11:03:42Z
last_indexed 2024-11-15T18:01:08Z
id cronfa63204
recordtype SURis
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spelling 2023-04-25T16:13:49.8264721 v2 63204 2023-04-19 Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2023-04-19 MACS 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. Conference Paper/Proceeding/Abstract 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) IEEE 1 12 2022 2022-12-01 10.1109/icmla55696.2022.00081 http://dx.doi.org/10.1109/icmla55696.2022.00081 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2023-04-25T16:13:49.8264721 2023-04-19T14:39:57.7162994 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sara Sharifzadeh 0000-0003-4621-2917 1
title Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
spellingShingle Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
Sara Sharifzadeh
title_short Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
title_full Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
title_fullStr Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
title_full_unstemmed Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
title_sort Spars Kernelized Features for Prediction of Rock’s Carbon Capture using 3D X-Ray Images
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Sara Sharifzadeh
format Conference Paper/Proceeding/Abstract
container_title 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
publishDate 2022
institution Swansea University
doi_str_mv 10.1109/icmla55696.2022.00081
publisher IEEE
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1109/icmla55696.2022.00081
document_store_str 0
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description 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.
published_date 2022-12-01T05:25:30Z
_version_ 1821381891573415936
score 11.3749895

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