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Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches

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  • Male, Frank
  • Jensen, Jerry L.
  • Lake, Larry W.

Abstract

Permeability prediction has been an important problem since the time of Darcy. Most approaches to solve this problem have used either idealized physical models or empirical relations. In recent years, machine learning (ML) has led to more accurate and robust, but less interpretable empirical models. Using 211 core samples collected from 12 wells in the Garn Sandstone from the North Sea, this study compared idealized physical models based on the Carman-Kozeny equation to interpretable ML models. We found that ML models trained on estimates of physical properties are more accurate than physical models. Also, the results show evidence of a threshold of about 10% volume fraction, above which pore-filling cement strongly affects permeability.

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  • Male, Frank & Jensen, Jerry L. & Lake, Larry W., 2020. "Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches," Earth Arxiv 3w6jx, Center for Open Science.
    • Handle: RePEc:osf:eartha:3w6jx
    DOI: 10.31219/osf.io/3w6jx
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    Cited by:

    1. Hera Khan & Ayush Srivastav & Amit Kumar Mishra & Tien Anh Tran, 2022. "Machine learning methods for estimating permeability of a reservoir," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2118-2131, October.

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