Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems
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
Affiliations
Sulieman Ibraheem Shelash Al-Hawary
Department of Business Administration, Business School, Al al-Bayt University, P.O.Box 130040, Mafraq, 25113, Jordan
Eyhab Ali
College of Chemistry, Al-Zahraa University for Women, Karbala, Iraq
Suhair Mohammad Husein Kamona
Department of Medical Laboratory Technics, Al-Manara College for Medical Sciences, Amarah, Iraq
Luma Hussain Saleh
Department of Anesthesia Techniques, Al-Noor University College, Nineveh, Iraq
Alzahraa S. Abdulwahid
Department of Medical Laboratory Technics, Al-Hadi University College, Baghdad, 10011, Iraq
Dahlia N. Al-Saidi
Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Iraq
Muataz S. Alhassan
Division of Advanced Nano Material Technologies, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq
Fadhil A. Rasen
Department of Medical Engineering, Al-Esraa University College, Baghdad, Iraq
Hussein Abdullah Abbas
College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq
Ahmed Alawadi
College 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, Iraq
Ali Hashim Abbas
College of Technical Engineering, Imam Ja’afar Al‐Sadiq University, Al-Muthanna, 66002, Iraq
Mohammad Sina
Department 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.
