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Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems
Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685191/ https://www.ncbi.nlm.nih.gov/pubmed/38034690 http://dx.doi.org/10.1016/j.heliyon.2023.e21913 |
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author | Ibraheem Shelash Al-Hawary, Sulieman Ali, Eyhab Mohammad Husein Kamona, Suhair Hussain Saleh, Luma Abdulwahid, Alzahraa S. Al-Saidi, Dahlia N. Alhassan, Muataz S. Rasen, Fadhil A. Abdullah Abbas, Hussein Alawadi, Ahmed Abbas, Ali Hashim Sina, Mohammad |
author_facet | Ibraheem Shelash Al-Hawary, Sulieman Ali, Eyhab Mohammad Husein Kamona, Suhair Hussain Saleh, Luma Abdulwahid, Alzahraa S. Al-Saidi, Dahlia N. Alhassan, Muataz S. Rasen, Fadhil A. Abdullah Abbas, Hussein Alawadi, Ahmed Abbas, Ali Hashim Sina, Mohammad |
author_sort | Ibraheem Shelash Al-Hawary, Sulieman |
collection | PubMed |
description | Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation. |
format | Online Article Text |
id | pubmed-10685191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106851912023-11-30 Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems Ibraheem Shelash Al-Hawary, Sulieman Ali, Eyhab Mohammad Husein Kamona, Suhair Hussain Saleh, Luma Abdulwahid, Alzahraa S. Al-Saidi, Dahlia N. Alhassan, Muataz S. Rasen, Fadhil A. Abdullah Abbas, Hussein Alawadi, Ahmed Abbas, Ali Hashim Sina, Mohammad Heliyon Research Article Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation. Elsevier 2023-11-07 /pmc/articles/PMC10685191/ /pubmed/38034690 http://dx.doi.org/10.1016/j.heliyon.2023.e21913 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Ibraheem Shelash Al-Hawary, Sulieman Ali, Eyhab Mohammad Husein Kamona, Suhair Hussain Saleh, Luma Abdulwahid, Alzahraa S. Al-Saidi, Dahlia N. Alhassan, Muataz S. Rasen, Fadhil A. Abdullah Abbas, Hussein Alawadi, Ahmed Abbas, Ali Hashim Sina, Mohammad Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems |
title | Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems |
title_full | Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems |
title_fullStr | Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems |
title_full_unstemmed | Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems |
title_short | Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems |
title_sort | prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685191/ https://www.ncbi.nlm.nih.gov/pubmed/38034690 http://dx.doi.org/10.1016/j.heliyon.2023.e21913 |
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