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Influence of Rock Properties on Structural Failure Probability—Caprock Shale Examples from the Horda Platform, Offshore Norway

Author

Listed:
  • Md Jamilur Rahman

    (Department of Geosciences, University of Oslo (UiO), 0371 Oslo, Norway)

  • Manzar Fawad

    (College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia
    Former UiO Researcher.)

  • Nazmul Haque Mondol

    (Department of Geosciences, University of Oslo (UiO), 0371 Oslo, Norway
    Norwegian Geotechnical Institute (NGI), 0806 Oslo, Norway)

Abstract

In any geological subsurface fluid injection, a viable top seal is required to contain the vertical movement of the injected fluid plume. However, seal integrity assessment is challenging because of the uncertainties possessed by various parameters. A probabilistic solution might be more appropriate when significant uncertainties are present. In this study, we evaluate Drake shale caprock structural reliability using a stochastic method. Drake shale is the primary top seal in the Aurora CO 2 storage site, located in the Horda Platform area in the northern North Sea. Based on the lithological variations, Drake caprock shale is divided into two parts designated by upper and lower units. Six model scenarios from the upper and lower Drake units have been tested. The probabilistic structural failures of varying model scenarios are estimated using the First-Order Reliability Method (FORM). Drake Formation shale shows a considerably low probability of failure (~0) with a high reliability index in the initial stress-state condition and after-injection scenarios. Moreover, the parameter sensitivity study indicates that horizontal stress and cohesion are the most influential input parameters during reliability estimation. Comparative analysis between the caprock properties and failure probability reveals that rock strength properties such as cohesion and friction angle strongly dictate the probability of failure estimation. Moreover, comparing two caprock shale formations indicates that the structural failure values are not correlatable; hence, a formation-specific failure assessment is recommended.

Suggested Citation

  • Md Jamilur Rahman & Manzar Fawad & Nazmul Haque Mondol, 2022. "Influence of Rock Properties on Structural Failure Probability—Caprock Shale Examples from the Horda Platform, Offshore Norway," Energies, MDPI, vol. 15(24), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9598-:d:1006688
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    References listed on IDEAS

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    1. Zhiyu Jiang & Weifei Hu & Wenbin Dong & Zhen Gao & Zhengru Ren, 2017. "Structural Reliability Analysis of Wind Turbines: A Review," Energies, MDPI, vol. 10(12), pages 1-25, December.
    2. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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