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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 2 of this Preprint.


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


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.



Chemical Engineering, Earth Sciences, Engineering, Hydrology, Multivariate Analysis, Petroleum Engineering, Physical Sciences and Mathematics, Statistics and Probability


petrophysics, Carman-Kozeny, Hybrid machine learning, Reservoir Characterization


Published: 2020-02-06 08:38

Last Updated: 2020-02-28 10:42

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CC BY Attribution 4.0 International

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