Calculating Equivalent Fracture Network Permeability of Multi-Layer-Complex Naturally Fractured Reservoirs

Abstract

Abstract Modeling naturally fractured reservoirs (NFRs) requires an accurate representation of fracture network permeability. Conventionally, logs, cores, seismic, and pressure transient tests are used as data base for this. Our previous attempts showed that a strong correlation exits between the fractal parameters of 2-D fracture networks and their permeability (Jafari and Babadagli, 2008, 2009a). We also showed that 1-D well (cores-logs) and 3-D reservoir data (well test) may not be sufficient in fracture network permeability (FNP) mapping and 2-D (outcrop) characteristics are needed (Jafari and Babadagli, 2009b). This paper is an extension of these studies where only 2-D (single layer, uniform fracture characteristics in z-direction) representations were used. In this paper, we considered a more complex and realistic 3-D network system. 2-D random fractures with known fractal and statistical characteristics were distributed in the x-, and y-directions. Variation of fracture network characteristics in the z-direction was presented by a multi layer system representing three different facieses with different fracture properties. Wells were placed in different locations of the model to collect 1-D fracture density and pressure transient data. In addition, five different fractal and statistical properties of the network of each layer were measured. The equivalent fracture network permeability (FNP) was calculated using a commercial software package as the base case. Using available 1-D, 2-D, and 3-D data, multivariable regression analyses were performed to obtain equivalent FNP correlations for many different fracture network realizations. The derived equations were validated against a new set of synthetic fracture networks and conditions at which 1-D, 2-D and 3-D are sufficient to map fracture network permeability were determined. The importance of the inclusion of each data type i.e. 1-D, 2-D and 3-D, in the correlations was discussed.