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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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Nonlin. Processes Geophys., 21, 9-18, 2014
https://doi.org/10.5194/npg-21-9-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
03 Jan 2014
Estimation of permeability of a sandstone reservoir by a fractal and Monte Carlo simulation approach: a case study
U. Vadapalli, R. P. Srivastava, N. Vedanti, and V. P. Dimri CSIR-National Geophysical Research Institute, Hyderabad, India
Abstract. Permeability of a hydrocarbon reservoir is usually estimated from core samples in the laboratory or from well test data provided by the industry. However, such data is very sparse and as such it takes longer to generate that. Thus, estimation of permeability directly from available porosity logs could be an alternative and far easier approach. In this paper, a method of permeability estimation is proposed for a sandstone reservoir, which considers fractal behavior of pore size distribution and tortuosity of capillary pathways to perform Monte Carlo simulations. In this method, we consider a reservoir to be a mono-dispersed medium to avoid effects of micro-porosity. The method is applied to porosity logs obtained from Ankleshwar oil field, situated in the Cambay basin, India, to calculate permeability distribution in a well. Computed permeability values are in good agreement with the observed permeability obtained from well test data. We also studied variation of permeability with different parameters such as tortuosity fractal dimension (Dt), grain size (r) and minimum particle size (d0), and found that permeability is highly dependent upon the grain size. This method will be extremely useful for permeability estimation, if the average grain size of the reservoir rock is known.

Citation: Vadapalli, U., Srivastava, R. P., Vedanti, N., and Dimri, V. P.: Estimation of permeability of a sandstone reservoir by a fractal and Monte Carlo simulation approach: a case study, Nonlin. Processes Geophys., 21, 9-18, https://doi.org/10.5194/npg-21-9-2014, 2014.
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