Optimized polymer flooding projects via combination of experimental design and reservoir simulation

The conventional approach for an EOR process is to compare the reservoir properties with those of successful worldwide projects. However, some proper cases may be neglected due to the lack of reliable data. A combination of experimental design and reservoir simulation is an alternative approach. In this work, the fractional factorial design suggests some numerical experiments which their results are analyzed by statistical inference. After determination of the main effects and interactions, the most important parameters of polymer flooding are studied by ANOVA method and Pareto and Tornado charts. Analysis of main effects shows that the oil viscosity, connate water saturation and the horizontal permeability are the 3 deciding factors in oil production. The proposed methodology can help to select the good candidate reservoirs for polymer flooding. Keywords: Polymer flooding, Fractional factorial design, Reservoir simulation, P-value, ANOV

Estimation of the Effective Permeability of Heterogeneous Porous Media by Using Percolation Concepts

In this paper we present new methods to estimate the effective permeability (k_eff) of heterogeneous porous media with a wide distribution of permeabilities and various underlying structures, using percolation concepts. We first set a threshold permeability (k_th) on the permeability density function (pdf) and use standard algorithms from percolation theory to check whether the high permeable grid blocks (i.e. those with permeability higher than k_th) with occupied fraction of “p” first forms a cluster connecting two opposite sides of the system in the direction of the flow (high permeability flow pathway). Then we estimate the effective permeability of the heterogeneous porous media in different ways: a power law (k_eff=k_th p^m), a weighted power average (k_eff=[p.k_th^m+(1-p).k_g^m ]^(1/m) with k_g the geometric average of the permeability distribution) and a characteristic shape factor multiplied by the permeability threshold value. We found that the characteristic parameters (i.e. the exponent “m”) can be inferred either from the statistics and properties of percolation sub-networks at the threshold point (i.e. high and low permeable regions corresponding to those permeabilities above and below the threshold permeability value) or by comparing the system properties with an uncorrelated random field having the same permeability distribution. These physically based approaches do not need fitting to the experimental data of effective permeability measurements to estimate the model parameter (i.e. exponent m) as is usually necessary in empirical methods. We examine the order of accuracy of these methods on different layers of 10th SPE model and found very good estimates as compared to the values determined from the commercial flow simulators