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

doi:10.1016/j.heliyon.2023.e21913

Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems

Heliyon. 2023 Nov 7;9(11):e21913. doi: 10.1016/j.heliyon.2023.e21913. eCollection 2023 Nov.

Affiliations

¹ Department of Business Administration, Business School, Al al-Bayt University, P.O.Box 130040, Mafraq, 25113, Jordan.
² College of Chemistry, Al-Zahraa University for Women, Karbala, Iraq.
³ Department of Medical Laboratory Technics, Al-Manara College for Medical Sciences, Amarah, Iraq.
⁴ Department of Anesthesia Techniques, Al-Noor University College, Nineveh, Iraq.
⁵ Department of Medical Laboratory Technics, Al-Hadi University College, Baghdad, 10011, Iraq.
⁶ Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Iraq.
⁷ Division of Advanced Nano Material Technologies, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq.
⁸ Department of Medical Engineering, Al-Esraa University College, Baghdad, Iraq.
⁹ College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq.
¹⁰ College of Technical Engineering, The Islamic University, Najaf, Iraq.
¹¹ College of Technical Engineering, The Islamic University of Al Diwaniyah, Iraq.
¹² College of Technical Engineering, The Islamic University of Babylon, Iraq.
¹³ College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna, 66002, Iraq.
¹⁴ Department of Petroleum Engineering, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.

Abstract

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.

Keywords: Autoregressive deep neural network; Carbon capture and storage (CCS); Geomechanics; Machine learning; Petrophysics; Shear wave velocity.