Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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
_version_ 1785151574997204992
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
work_keys_str_mv AT ibraheemshelashalhawarysulieman predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT alieyhab predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT mohammadhuseinkamonasuhair predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT hussainsalehluma predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT abdulwahidalzahraas predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT alsaididahlian predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT alhassanmuatazs predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT rasenfadhila predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT abdullahabbashussein predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT alawadiahmed predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT abbasalihashim predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems
AT sinamohammad predictionofgeomechanicalbearingcapacityusingautoregressivedeepneuralnetworkincarboncaptureandstoragesystems