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Deriving Relative Permeability from Capillary Pressure Using Gaussian and Rational Equations

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While dynamic data are necessary for a robust reservoir characterization, measuring these type of data in a laboratory is time consuming and very expensive. On the other hand, if dynamic data, especially relative permeability and capillary pressure, are available for discrete grids, they might lead to a more promising simulation model. In the following study, capillary pressure is predicted by artificial neural networks for distinct flow units. Then, two methods are introduced for estimating relative permeability: the first one is based on using Gaussian and rational equations for deriving relative permeability from capillary pressure data and the second one is by utilizing ANN.
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Keywords: Gaussian equation; capillary pressure; flow unit; neural network; relative permeability

Document Type: Research Article

Affiliations: 1: Petroleum University of Technology Research Center, Tehran, Iran 2: Institute of Petroleum Engineering, University of Tehran, Tehran, Iran

Publication date: August 3, 2014

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