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Porosity Modeling

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Abstract

Porosity is one of the basic petrophysical properties because it provides the necessary storage for hydrocarbon accumulation. Determining the porosity distribution of a reservoir is a necessary step in describing the pores in subsurface formations. Generally, it is also a good practice to model porosity before modeling other petrophysical properties because porosity typically has more data. Other petrophysical properties, such as fluid saturation and permeability, are usually correlated to porosity and can be modeled on the basis of their relationship after the porosity model is constructed.

The more storage you have, the more stuff you can accumulate.

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References

  • Ma, Y. Z. (2010). Error types in reservoir characterization and management. Journal of Petroleum Science and Engineering. https://doi.org/10.1016/j.petrol.2010.03.030.

    Article  Google Scholar 

  • Ma, Y. Z., & Royer, J. J. (1994). Optimal filtering for non-stationary images. IEEE 8th Workshop on IMDSP, pp. 88–89.

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  • Ma, Y. Z., Seto, A., & Gomez, E. (2008). Frequentist meets spatialist: A marriage made in reservoir characterization and modeling. SPE 115836, SPE ATCE, Denver, CO, USA.

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  • Matheron, G. (1973). The intrinsic random functions and their applications. Advances in Applied Probability, 5, 439–468.

    Article  MathSciNet  Google Scholar 

  • Xu, W. (1996). Conditional curvilinear stochastic simulation using pixel-based algorithms. Mathematical Geology, 28(7), 937–949.

    Article  Google Scholar 

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Ma, Y.Z. (2019). Porosity Modeling. In: Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling. Springer, Cham. https://doi.org/10.1007/978-3-030-17860-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-17860-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17859-8

  • Online ISBN: 978-3-030-17860-4

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