Deriving Relative Permeability from Capillary Pressure Using Gaussian and Rational Equations
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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
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