**1. Introduction**

The extremely low permeability of the common shale plays means that simple, bi-planar hydraulic fractures (HF) do not create enough surface area to make economic wells and that stimulation of the natural fracture system is critical [1]. Numerous field microseismic data sets have shown that extreme fracture complexity may result from the interaction between a created hydraulic fracture and the pre-existing fracture network [2, 3]. Consequently, operators will often alter the stimulation design, by changing injection rate, viscosity, or other parame‐ ters, in order to improve the effectiveness of the stimulation in unconventional shale plays.

© 2013 Zhang et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

However, these design changes offer only a limited control on improving the stimulation of natural fractures because of a lack of understanding of the fundamental characteristics, behavior, and connectivity of the natural fracture network.

effectiveness and microseismicity generation. For the study, a DFN generator was developed that was capable of creating a fracture network that satisfied the assigned input statistical

Fracture Network Connectivity — A Key To Hydraulic Fracturing Effectiveness and Microseismicity Generation

As shown in Figure 1, two DFN configurations were realized, which represent a sparse DFN and a dense DFN, respectively. The sparse DFN had 191 fractures while the dense DFN had 482 fractures, or about 2.5 times more than the sparse DFN. The fractures were created in the

construct two orthogonal fracture sets. The P10 (the number of fractures along a line divided by the length of the line) for the sparse DFN and dense DFN was 0.079 m-1 and 0.28 m-1, respectively. The P32 (the sum of the areas of the fractures contained in a named volume

In addition to DFN density, the effect of the initial DFN aperture was studied by considering two different values of initial aperture for the DFN fractures. In the first case, it was assumed that the initial aperture of the DFN fractures was equal to 0.1 mm. In the second case, it was

The goal of this study was to investigate the effects of hydraulic fracturing in shale formations with different levels of DFN connectivity. It was assumed that the DFN fractures were static, meaning that, with the exception of the main hydraulic fracture itself, the process of fracture

**Figure 1.** Plan view of two DFN realizations. A) Sparse DFN with 191 fractures; and B) Dense DFN with 482 fractures.

The numerical code used in the simulations, *3DEC,* is a three dimensional distinct element method for discontinuum modeling [11]. It can simulate the response of discontinuous media,

but either a 60° or 150°

dip direction in order to

http://dx.doi.org/10.5772/56302

/m3

and 0.075

593

characteristics and allowed for the quantitative variation of network connectivity.

divided by the same volume) for the sparse DFN and dense DFN was 0.031 m2

assumed that the DFN fracture aperture was 1.5 times greater and equal to 0.15 mm.

growth and propagation was not considered within the simulations.

common disk shape with a dip angle of 90°

m2 /m3

, respectively.

**3. Numerical simulations**

**3.1.** *3DEC* **hydraulic fracturing modeling capabilities**

The connectivity of the fracture network, for example, determines the overall hydraulic diffusivity of the formation and is a key to the resulting 'complexity' from a hydraulic fracture stimulation. A highly connected fracture network will allow more fluid leakoff into the rock mass and render pressure communication over large distances, whereas a partially or sparsely connected fracture network will favor the propagation of a new hydraulic fracture and may exhibit pressure isolation between very closely spaced hydraulic fractures. The intricacy of fluid flow in fractured formation is mainly due to the complex geometries, patterns, and heterogeneity of the fracture network. The fracture network connectivity, therefore, has been shown to be a critical factor which affects treating pressures, the created microseismicity and corresponding SRV (Stimulated Rock Volume), and production.

Numerous numerical modeling efforts have been conducted in order to understand the process of hydraulic fracture (HF) interaction with a complex natural fracture network [4, 5, 6]. However, relatively few works have focused on understanding the role of natural fracture network connectivity and its impact on the effectiveness of hydraulic fracturing of shale reservoirs and associated microseismicity generation.

In this paper, a discussion of fracture network connectivity and how it is utilized in developing a discrete fracture network (DFN) is presented, which is then incorporated into a discrete element numerical model (DEM). The propagation of a HF in the fractured rock mass was then studied using the DEM, which allowed for fully coupled, hydro-mechanical simulations, including the generation of synthetic microseismicity. Following previous work [7, 8, 9, 10] from the authors on parametric studies to analyze the influence of the mechanical and flow properties of the DFN and matrix on HF propagation, the influence of the DFN fracture network connectivity was analyzed in this work using common stimulation metrics such as SRV. The corresponding microseismic response was also calculated from the simulation results and related to the effective SRV. The results show not only the critical role that the DFN must play in resource evaluations, but also in completion design and stimulation optimization.

### **2. Fracture network connectivity and DFN realization**

Fracture network connectivity is determined by many statistical characteristics, among them, fracture shape, fracture size distribution, fracture density (area of fracture per unit volume), orientation distribution, and aperture size distribution. The combinations of these statistical characteristics that describe the geometrical properties of a DFN define the macro-scale connectivity and directional flow preference of the DFN, and thus, are essential for the fluid transport characterization of an unconventional reservoir.

In this work, focus was placed on the effects of two statistical fracture characteristics, fracture density and initial aperture both individually and in combination, on hydraulic fracturing effectiveness and microseismicity generation. For the study, a DFN generator was developed that was capable of creating a fracture network that satisfied the assigned input statistical characteristics and allowed for the quantitative variation of network connectivity.

As shown in Figure 1, two DFN configurations were realized, which represent a sparse DFN and a dense DFN, respectively. The sparse DFN had 191 fractures while the dense DFN had 482 fractures, or about 2.5 times more than the sparse DFN. The fractures were created in the common disk shape with a dip angle of 90° but either a 60° or 150° dip direction in order to construct two orthogonal fracture sets. The P10 (the number of fractures along a line divided by the length of the line) for the sparse DFN and dense DFN was 0.079 m-1 and 0.28 m-1, respectively. The P32 (the sum of the areas of the fractures contained in a named volume divided by the same volume) for the sparse DFN and dense DFN was 0.031 m2 /m3 and 0.075 m2 /m3 , respectively.

In addition to DFN density, the effect of the initial DFN aperture was studied by considering two different values of initial aperture for the DFN fractures. In the first case, it was assumed that the initial aperture of the DFN fractures was equal to 0.1 mm. In the second case, it was assumed that the DFN fracture aperture was 1.5 times greater and equal to 0.15 mm.

The goal of this study was to investigate the effects of hydraulic fracturing in shale formations with different levels of DFN connectivity. It was assumed that the DFN fractures were static, meaning that, with the exception of the main hydraulic fracture itself, the process of fracture growth and propagation was not considered within the simulations.

**Figure 1.** Plan view of two DFN realizations. A) Sparse DFN with 191 fractures; and B) Dense DFN with 482 fractures.
