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...
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Format: | Article |
Language: | English |
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Elsevier
2023-11-01
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Series: | Heliyon |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023091211 |
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author | Sulieman Ibraheem Shelash Al-Hawary Eyhab Ali Suhair Mohammad Husein Kamona Luma Hussain Saleh Alzahraa S. Abdulwahid Dahlia N. Al-Saidi Muataz S. Alhassan Fadhil A. Rasen Hussein Abdullah Abbas Ahmed Alawadi Ali Hashim Abbas Mohammad Sina |
author_facet | Sulieman Ibraheem Shelash Al-Hawary Eyhab Ali Suhair Mohammad Husein Kamona Luma Hussain Saleh Alzahraa S. Abdulwahid Dahlia N. Al-Saidi Muataz S. Alhassan Fadhil A. Rasen Hussein Abdullah Abbas Ahmed Alawadi Ali Hashim Abbas Mohammad Sina |
author_sort | Sulieman Ibraheem Shelash Al-Hawary |
collection | DOAJ |
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. |
first_indexed | 2024-03-09T09:18:10Z |
format | Article |
id | doaj.art-d1e791451dc54c12872500215be7db84 |
institution | Directory Open Access Journal |
issn | 2405-8440 |
language | English |
last_indexed | 2024-03-09T09:18:10Z |
publishDate | 2023-11-01 |
publisher | Elsevier |
record_format | Article |
series | Heliyon |
spelling | doaj.art-d1e791451dc54c12872500215be7db842023-12-02T07:04:39ZengElsevierHeliyon2405-84402023-11-01911e21913Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systemsSulieman Ibraheem Shelash Al-Hawary0Eyhab Ali1Suhair Mohammad Husein Kamona2Luma Hussain Saleh3Alzahraa S. Abdulwahid4Dahlia N. Al-Saidi5Muataz S. Alhassan6Fadhil A. Rasen7Hussein Abdullah Abbas8Ahmed Alawadi9Ali Hashim Abbas10Mohammad Sina11Department of Business Administration, Business School, Al al-Bayt University, P.O.Box 130040, Mafraq, 25113, JordanCollege of Chemistry, Al-Zahraa University for Women, Karbala, IraqDepartment of Medical Laboratory Technics, Al-Manara College for Medical Sciences, Amarah, IraqDepartment of Anesthesia Techniques, Al-Noor University College, Nineveh, IraqDepartment of Medical Laboratory Technics, Al-Hadi University College, Baghdad, 10011, IraqDepartment of Medical Laboratories Technology, AL-Nisour University College, Baghdad, IraqDivision of Advanced Nano Material Technologies, Scientific Research Center, Al-Ayen University, Thi-Qar, IraqDepartment of Medical Engineering, Al-Esraa University College, Baghdad, IraqCollege of Technical Engineering, National University of Science and Technology, Dhi Qar, IraqCollege of Technical Engineering, The Islamic University, Najaf, Iraq; College of Technical Engineering, The Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, The Islamic University of Babylon, IraqCollege of Technical Engineering, Imam Ja’afar Al‐Sadiq University, Al-Muthanna, 66002, IraqDepartment of Petroleum Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2405844023091211Carbon capture and storage (CCS)Machine learningShear wave velocityAutoregressive deep neural networkGeomechanicsPetrophysics |
spellingShingle | Sulieman Ibraheem Shelash Al-Hawary Eyhab Ali Suhair Mohammad Husein Kamona Luma Hussain Saleh Alzahraa S. Abdulwahid Dahlia N. Al-Saidi Muataz S. Alhassan Fadhil A. Rasen Hussein Abdullah Abbas Ahmed Alawadi Ali Hashim Abbas Mohammad Sina Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems Heliyon Carbon capture and storage (CCS) Machine learning Shear wave velocity Autoregressive deep neural network Geomechanics Petrophysics |
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 | Carbon capture and storage (CCS) Machine learning Shear wave velocity Autoregressive deep neural network Geomechanics Petrophysics |
url | http://www.sciencedirect.com/science/article/pii/S2405844023091211 |
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