**4. Comparison of hydraulic fracturing simulations with OpenT versus eRP**

#### **4.1. Influence of viscosity**

[14,15] accounts for the local stress field, pore pressure, crossing angle, rock tensile strength

**Figure 3.** Comparison between analytical models given in [13,14,15], OpenT [21], and the experimental results [15]

**Figure 4.** Comparison between numerical crossing-arresting HF-NF behavior using MineHF2D code [4,5,8]. The red crosses and squares respectively indicate crossing and arresting behavior from MineHF2D code, solid green curves cor‐ respond to analytical predictions using OpenT [21], dash yellow curve corresponds to Blanton criterion [13], and eRP criterion [14,15] is given by dash blue lines. The interaction is studied for various injection rate and relative stress dif‐

ference for two different HF-NF contact angles, β=90⁰ (left) and β=60⁰ (right).

and frictional properties of the natural fractures.

190 Effective and Sustainable Hydraulic Fracturing

The comparison of results generated using two crossing criteria - eRP criterion [14,15] and new OpenT criterion [20, 21] - is presented in Figure 5 and Figure 6 for a simple example given in Table 1 (values shown in italic are used only in OpenT crossing criterion). The cohesion and toughness of natural fracture are considered to be negligible.


**Table 1.** Input data Example 1

For the case of lower fluid viscosity (Figure 5a and Figure 6a) both criteria show similar hydraulic fracture patterns with no crossing of the natural fractures. For higher viscosity fluid OpenT crossing criterion shows that hydraulic fractures cross the NF#1 and NF#3 (Figure 6b), while with eRP the hydraulic fracture network (HFN) pattern does not change. The intersection angle between HF and NF#1 was 62.5 deg, between HF and NF#2 was 15 deg, and the interaction angle between HF and NF#3 was 75 deg.

deg.

K'=0.01Pa-s (right)

**Figure 5.** Hydraulic fracture networks generated for Example 1 with eRP crossing criterion [14] with fluid viscosity K'=0.001Pa-s (left), and K'=0.01Pa-s (right)

**Figure 7.** Proppant placement prediction for Example 1 from eRP (left) and OpenT (right) criteria with fluid K'=0.01Pa-

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

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For the case of low viscosity fluid (Figure 8 left) both criteria show similar hydraulic fracture patterns with mostly no crossing of the natural fractures. When fracturing fluid viscosity was increased, considerable differences in patterns have been observed (Figure 8 right). The results for eRP approach stay mostly the same, showing that fluid eventually penetrated into the NF and opens it. But the simulation based on OpenT criteria shows that for higher viscosity fluid hydraulic fracture intersects most of the natural fractures, resulting in a bi-wing like HFN

The example presented on Figure 8b is consistent with the general observations that hydraulic fracturing treatments with higher fluid viscosity HFN tend to cross natural fractures and

s after 100 min of shut-in. Slurry is shown in light blue, bank is in dark blue, and clean fluid is in orange.

*Injection rate* 0.13 m3/s Stress anisotropy 2 MPa Young's modulus 3.5×1010 Pa

Poisson's ratio 0.25

Fluid Specific Gravity 1.0 Min horizontal stress 42.7 MPa Max horizontal stress 44.6 MPa Fracture toughness 1 MPa-m0.5 Tensile strength 3.4 MPa NF friction Coefficient 0.4 *NF permeability* 1 Darcy

**Table 2.** Input data for Example 2

*Fluid viscosity* 0.0004-0.04 Pa-s

pattern, which will produce a narrow microseismic events cloud.

Figure 5. Hydraulic fracture networks generated for Example 1 with eRP crossing criterion [14] with fluid viscosity K'=0.001Pa-s (left), and

**Figure 6.** Hydraulic fracture networks generated for Example 1 with OpenT crossing criterion [21] with fluid viscosity K'=0.001pa-s (left) and K'=0.01Pa-s (right)

So, while eRP criterion gives for this case the same prediction (no crossing) for both low and high viscosity fluids, OpenT criterion predicts crossing the NF with higher crossing angle for the more viscous fluid.

Differences in the predicted hydraulic fracture network result in different proppant placement (Figure 7), and will result in differences in production evaluation and prediction.

Example 2 with more dramatic output differences is presented in Figure 8 for the same pumping schedule, zone properties, fluid and natural fractures properties. In Table 2 the main input data is shown (values shown in italic are used only in OpenT crossing criterion), the toughness and cohesion of natural fractures are considered to be negligible. Natural fractures are oriented mostly perpendicular (~ 90deg) to the maximum horizontal stress direction, i.e. to the preferred direction of hydraulic fracture propagation.

**Figure 7.** Proppant placement prediction for Example 1 from eRP (left) and OpenT (right) criteria with fluid K'=0.01Pas after 100 min of shut-in. Slurry is shown in light blue, bank is in dark blue, and clean fluid is in orange.


**Table 2.** Input data for Example 2

a) Low viscosity, no crossing b) higher viscosity, no crossing

Fracture toughness 1.3 MPa-m0.5 Tensile strength 3.5 MPa NF friction Coefficient 0.5 NF permeability 1 Darcy

K'=0.001Pa-s (left), and K'=0.01Pa-s (right)

192 Effective and Sustainable Hydraulic Fracturing

K'=0.001pa-s (left) and K'=0.01Pa-s (right)

the more viscous fluid.

Table 1. Input data Example 1

deg.

K'=0.01Pa-s (right)

**Figure 5.** Hydraulic fracture networks generated for Example 1 with eRP crossing criterion [14] with fluid viscosity

Figure 5. Hydraulic fracture networks generated for Example 1 with eRP crossing criterion [14] with fluid viscosity K'=0.001Pa-s (left), and

a) Low viscosity, no crossing b) Higher viscosity, crossing NF#1and NF#3

**Figure 6.** Hydraulic fracture networks generated for Example 1 with OpenT crossing criterion [21] with fluid viscosity

So, while eRP criterion gives for this case the same prediction (no crossing) for both low and high viscosity fluids, OpenT criterion predicts crossing the NF with higher crossing angle for

Differences in the predicted hydraulic fracture network result in different proppant placement

Example 2 with more dramatic output differences is presented in Figure 8 for the same pumping schedule, zone properties, fluid and natural fractures properties. In Table 2 the main input data is shown (values shown in italic are used only in OpenT crossing criterion), the toughness and cohesion of natural fractures are considered to be negligible. Natural fractures are oriented mostly perpendicular (~ 90deg) to the maximum horizontal stress direction, i.e.

(Figure 7), and will result in differences in production evaluation and prediction.

to the preferred direction of hydraulic fracture propagation.

For the case of lower fluid viscosity (Figure 5a and Figure 6a) both criteria show similar hydraulic fracture patterns with no crossing of the natural fractures. For higher viscosity fluid OpenT crossing criterion shows that hydraulic fractures cross the NF#1 and NF#3 (Figure 6b), while with eRP the hydraulic fracture network (HFN) pattern does not change. The intersection angle between HF and NF#1 was 62.5 deg, between HF and NF#2 was 15 deg, and the interaction angle between HF and NF#3 was 75

> For the case of low viscosity fluid (Figure 8 left) both criteria show similar hydraulic fracture patterns with mostly no crossing of the natural fractures. When fracturing fluid viscosity was increased, considerable differences in patterns have been observed (Figure 8 right). The results for eRP approach stay mostly the same, showing that fluid eventually penetrated into the NF and opens it. But the simulation based on OpenT criteria shows that for higher viscosity fluid hydraulic fracture intersects most of the natural fractures, resulting in a bi-wing like HFN pattern, which will produce a narrow microseismic events cloud.

> The example presented on Figure 8b is consistent with the general observations that hydraulic fracturing treatments with higher fluid viscosity HFN tend to cross natural fractures and

generate a narrower fracture network, while for low viscosity fluids it is easier to penetrate into the natural fracture and open it [9] and generate a wider fracture network.

Notice that while using eRP criterion, the change in pumping rate can change fracture footprint due to change in fluid pressure, width, and therefore local stresses and crossing angle, while OpenT model introduces additional change due to the rate effect on the crossing behaviour.

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

The cases presented in Examples 1-2 above, demonstrated the impact of fluid viscosity. Now these examples will be considered again to demonstrate the impact of pumping rate, which is also accounted for in the new crossing model. The base case of pumping rate *Q=*0.132 m3

considered and compared with additional cases when rate is changed (Table 3). The total pumping time in schedule was changed accordingly for different pumping rates to maintain

First, on Figures 9a-10a the results of using the eRP crossing model with different rates as given in Table 3, and two types of fluid viscosity are presented for Example 1 and compared with the same simulations using OpenT crossing model (Figures 9b-10b). Due to relatively high leakoff coefficient used in the presented case the fracture network for higher rate is larger due

a) Hydraulic fracture networks generated with eRP crossing criterion for low viscosity fluid (K'=0.001Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

b) Hydraulic fracture networks generated with OpenT crossing criterion for low viscosity fluid (K'=0.001Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

**Figure 9.** Influence of Pumping Rate: Hydraulic fracture networks generated for Example 1 with low viscosity fluid and

with eRP (a) and OpenT(b) crossing models at injection rates from Table 3

Fluid viscosity 0.0004-0.04 Pa-s Injection rate : Q 0.132 m3/s Injection rate: Q/2 0.066 m3/s Injection rate: 5Q 0.66 m3/s

the same total fluid volume.

to greater fluid efficiency.

**Table 3.** Input data to test impact of pumping rate

/s is

195

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 a) HFN predicted for Example 2 with eRP criterion for low viscosity fluid on the left (K'=0.0004Pa-s), and higher viscosity

b) HFN predicted for Example 2 with OpenT criterion for low viscosity fluid on the left (K'=0.0004Pa-s), and higher viscosity fluid on the right (K'=0.04Pa-s)

**Figure 8.** Hydraulic fracture networks generated for Example 2 with eRP crossing criterion (a) and OpenT crossing cri‐ terion (b) for low and high fluid viscosity cases. The pre-existing DFN is also shown

It should be mentioned that rock properties, crossing angle between natural fracture and hydraulic fracture, and natural fractures properties all work together with fluid properties to define the crossing pattern and the resulting fracture footprint. This paper intends to empha‐ size the importance of fluid properties to be included into the general consideration for HF/NF interaction prediction.

#### **4.2. Influence of pumping rates**

As it was mentioned before, injection rate works together with fluid viscosity when HF interacts with NF [9, 18, 19]. In the OpenT crossing model the injection rate is also taken into account to predict (evaluate) HF/NF crossing or opening (Equation 1).

Notice that while using eRP criterion, the change in pumping rate can change fracture footprint due to change in fluid pressure, width, and therefore local stresses and crossing angle, while OpenT model introduces additional change due to the rate effect on the crossing behaviour.

The cases presented in Examples 1-2 above, demonstrated the impact of fluid viscosity. Now these examples will be considered again to demonstrate the impact of pumping rate, which is also accounted for in the new crossing model. The base case of pumping rate *Q=*0.132 m3 /s is considered and compared with additional cases when rate is changed (Table 3). The total pumping time in schedule was changed accordingly for different pumping rates to maintain the same total fluid volume.


**Table 3.** Input data to test impact of pumping rate

generate a narrower fracture network, while for low viscosity fluids it is easier to penetrate

a) HFN predicted for Example 2 with eRP criterion for low viscosity fluid on the left (K'=0.0004Pa-s), and higher viscosity fluid on the right (K'=0.04Pa-s)

b) HFN predicted for Example 2 with OpenT criterion for low viscosity fluid on the left (K'=0.0004Pa-s), and higher viscosity fluid on the right (K'=0.04Pa-s)

**Figure 8.** Hydraulic fracture networks generated for Example 2 with eRP crossing criterion (a) and OpenT crossing cri‐

It should be mentioned that rock properties, crossing angle between natural fracture and hydraulic fracture, and natural fractures properties all work together with fluid properties to define the crossing pattern and the resulting fracture footprint. This paper intends to empha‐ size the importance of fluid properties to be included into the general consideration for

As it was mentioned before, injection rate works together with fluid viscosity when HF interacts with NF [9, 18, 19]. In the OpenT crossing model the injection rate is also taken into

terion (b) for low and high fluid viscosity cases. The pre-existing DFN is also shown

account to predict (evaluate) HF/NF crossing or opening (Equation 1).

HF/NF interaction prediction.

194 Effective and Sustainable Hydraulic Fracturing

**4.2. Influence of pumping rates**

into the natural fracture and open it [9] and generate a wider fracture network.

First, on Figures 9a-10a the results of using the eRP crossing model with different rates as given in Table 3, and two types of fluid viscosity are presented for Example 1 and compared with the same simulations using OpenT crossing model (Figures 9b-10b). Due to relatively high leakoff coefficient used in the presented case the fracture network for higher rate is larger due to greater fluid efficiency.

 a) Hydraulic fracture networks generated with eRP crossing criterion for low viscosity fluid (K'=0.001Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

b) Hydraulic fracture networks generated with OpenT crossing criterion for low viscosity fluid (K'=0.001Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

**Figure 9.** Influence of Pumping Rate: Hydraulic fracture networks generated for Example 1 with low viscosity fluid and with eRP (a) and OpenT(b) crossing models at injection rates from Table 3

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

Figure10a). Results for the test case of Example 2 are given in Figures 11-12.

Figure10a). Results for the test case of Example 2 are given in Figures 11-12.

various injection rates (0.066 m3

various injection rates (0.066 m3

and OpenT(b) crossing models and at injection rates from Table 3

and OpenT(b) crossing models and at injection rates from Table 3

ing models for high viscosity fluid at injection rates from Table 3

with eRP (a) and OpenT(b) crossing models at injection rates from Table 3

so HFN can propagate faster. Again, the resulting HFN with eRP model does not depend on the fluid viscosity (Figure 9a and

so HFN can propagate faster. Again, the resulting HFN with eRP model does not depend on the fluid viscosity (Figure 9a and

a) Hydraulic fracture networks generated with eRP crossing criterion for low viscosity fluid (K'=0.0004Pa-s) with

a) Hydraulic fracture networks generated with eRP crossing criterion for low viscosity fluid (K'=0.0004Pa-s) and with

b) Hydraulic fracture networks generated with OpenT crossing model for low viscosity fluid (K'=0.0004Pa-s) with

Figure 11. Influence of Pumping Rate: Hydraulic fracture networks generated with low viscosity fluid for Example 2 with eRP (a)

b) Hydraulic fracture networks generated with OpenT crossing model for low viscosity fluid (K'=0.0004Pa-s) and with

**Figure 11.** Influence of Pumping Rate: Hydraulic fracture networks generated with low viscosity fluid for Example 2

/s) Figure 11. Influence of Pumping Rate: Hydraulic fracture networks generated with low viscosity fluid for Example 2 with eRP (a)

/s)

a) Hydraulic fracture networks generated with eRP crossing model for higher viscosity fluid (K'=0.04Pa-s) and with various

a) Hydraulic fracture networks generated with eRP crossing model for higher viscosity fluid (K'=0.04Pa-s) with various

b) Hydraulic fracture networks generated with OpenT crossing model for higher viscosity fluid (K'=0.04Pa-s) and with

b) Hydraulic fracture networks generated with OpenT crossing model for higher viscosity fluid (K'=0.04Pa-s) with

**Figure 12.** Influence of Pumping Rate: Hydraulic fracture networks generated for Example 2 with old and new cross‐

/s, 0.132 m3

/s, 0.132 m3

/s, 0.132 m3

/s, 0.132 m3

/s, and 0.66 m3

/s, and 0.66 m3

/s, and 0.66 m3

/s, and 0.66 m3

/s)

/s)

/s)

/s)

injection rates (0.066 m3

injection rates (0.066 m3

various injection rates (0.066 m3

various injection rates (0.066 m3

/s, and 0.66 m3

/s, and 0.66 m3

/s, 0.132 m3

/s, 0.132 m3

/s, 0.132 m3

/s, 0.132 m3

/s, and 0.66 m3

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

/s, and 0.66 m3

/s)

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197

/s)

various injection rates (0.066 m3

various injection rates (0.066 m3

10

10

 a) Hydraulic fracture networks generated with eRP crossing criterion for higher viscosity fluid (K'=0.01Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

b) Hydraulic fracture networks generated with OpenT crossing criterion for higher viscosity fluid (K'=0.01Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

**Figure 10.** Influence of Pumping Rate: Hydraulic fracture networks generated with higher viscosity fluid for Example 1 with eRP (a) and OpenT(b) crossing models at injection rates from Table 3

The first observation is that for low injection rate and for both high and low fluid viscosities fracture network is similar with both crossing models for this simple Example 1, and no crossing is observed. When injection rate is increased, HFN becomes more complex: OpenT shows crossing at the first natural fracture at the angle of 62.5 deg with both low and high viscosity fluids. The eRP criterion does not show crossing, and network complexity is due to the smaller time required to open NF and higher injection rates, so HFN can propagate faster. Again, the resulting HFN with eRP model does not depend on the fluid viscosity (Figure 9a and Figure10a). Results for the test case of Example 2 are given in Figures 11-12.

As we can see from Figures 11a and 12a, eRP criterion exhibits similar HFN footprints for both low and high viscosity fluids and for different injection rates. The reason for the relative insensitivity to the injection rate as compared to Example 1 is due to the higher stress aniso‐ tropy for Example 2.

With OpenT crossing criterion, for lower fluid viscosity the chance of crossing perpendicular NF increases with increasing injection rate. At the same time for a higher viscosity fluid, while it can cross the natural fracture more easily, it is more difficult to open the crossed natural fracture. The observed behaviour with new crossing model is consistent with experimental observations [9].

So we can conclude from the observations in these cases that the fluid viscosity together with pumping rate could play a major role on the crossing. At the same time the influence of pumping rate is not as strong as viscosity, and mostly affects the opening of the intersected natural fractures.

so HFN can propagate faster. Again, the resulting HFN with eRP model does not depend on the fluid viscosity (Figure 9a and

Figure10a). Results for the test case of Example 2 are given in Figures 11-12.

and OpenT(b) crossing models and at injection rates from Table 3

a) Hydraulic fracture networks generated with eRP crossing criterion for low viscosity fluid (K'=0.0004Pa-s) with various injection rates (0.066 m3 /s, 0.132 m3 /s, and 0.66 m3 /s)

196 Effective and Sustainable Hydraulic Fracturing

with eRP (a) and OpenT(b) crossing models at injection rates from Table 3

tropy for Example 2.

observations [9].

natural fractures.

a) Hydraulic fracture networks generated with eRP crossing criterion for higher viscosity fluid (K'=0.01Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

b) Hydraulic fracture networks generated with OpenT crossing criterion for higher viscosity fluid (K'=0.01Pa-s) with various injection rates (0.066 m3/s, 0.132 m3/s, and 0.66 m3/s)

**Figure 10.** Influence of Pumping Rate: Hydraulic fracture networks generated with higher viscosity fluid for Example 1

The first observation is that for low injection rate and for both high and low fluid viscosities fracture network is similar with both crossing models for this simple Example 1, and no crossing is observed. When injection rate is increased, HFN becomes more complex: OpenT shows crossing at the first natural fracture at the angle of 62.5 deg with both low and high viscosity fluids. The eRP criterion does not show crossing, and network complexity is due to the smaller time required to open NF and higher injection rates, so HFN can propagate faster. Again, the resulting HFN with eRP model does not depend on the fluid viscosity (Figure 9a

As we can see from Figures 11a and 12a, eRP criterion exhibits similar HFN footprints for both low and high viscosity fluids and for different injection rates. The reason for the relative insensitivity to the injection rate as compared to Example 1 is due to the higher stress aniso‐

With OpenT crossing criterion, for lower fluid viscosity the chance of crossing perpendicular NF increases with increasing injection rate. At the same time for a higher viscosity fluid, while it can cross the natural fracture more easily, it is more difficult to open the crossed natural fracture. The observed behaviour with new crossing model is consistent with experimental

So we can conclude from the observations in these cases that the fluid viscosity together with pumping rate could play a major role on the crossing. At the same time the influence of pumping rate is not as strong as viscosity, and mostly affects the opening of the intersected

and Figure10a). Results for the test case of Example 2 are given in Figures 11-12.

b) Hydraulic fracture networks generated with OpenT crossing model for low viscosity fluid (K'=0.0004Pa-s) with various injection rates (0.066 m3 /s, 0.132 m3 /s, and 0.66 m3 /s)

Figure 11. Influence of Pumping Rate: Hydraulic fracture networks generated with low viscosity fluid for Example 2 with eRP (a)

Figure 11. Influence of Pumping Rate: Hydraulic fracture networks generated with low viscosity fluid for Example 2 with eRP (a) and OpenT(b) crossing models and at injection rates from Table 3 **Figure 11.** Influence of Pumping Rate: Hydraulic fracture networks generated with low viscosity fluid for Example 2 with eRP (a) and OpenT(b) crossing models at injection rates from Table 3 b) Hydraulic fracture networks generated with OpenT crossing model for low viscosity fluid (K'=0.0004Pa-s) and with various injection rates (0.066 m3 /s, 0.132 m3 /s, and 0.66 m3 /s)

b) Hydraulic fracture networks generated with OpenT crossing model for higher viscosity fluid (K'=0.04Pa-s) with various injection rates (0.066 m3 /s, 0.132 m3 /s, and 0.66 m3 /s)

various injection rates (0.066 m3

b) Hydraulic fracture networks generated with OpenT crossing model for higher viscosity fluid (K'=0.04Pa-s) and with

/s, 0.132 m3

/s, and 0.66 m3

/s)

10

10 **Figure 12.** Influence of Pumping Rate: Hydraulic fracture networks generated for Example 2 with old and new cross‐ ing models for high viscosity fluid at injection rates from Table 3

#### **4.3. Barnett example**

To further validate the model in a realistic field condition, we examine a synthetic case that mimics the field example in Barnett Shale presented by Warpinski et al. [16] as shown in Figure 1. Though the details of the well and formation data and pumping schedule are not exactly replicated, the synthetic case is created using the data that is available in [16], so the well and formation configurations are very close to the real case.

For the complex fracture simulation, detailed vertical stress profile is not available from [16]. Instead a fixed height model is used based on the microseismic measurements presented in [16]. It is assumed that the fracture height is 310 ft covering Lower Barnett for the case of crosslinked gel treatment, and 360 ft for the slick water treatment. For the simulation of slick water refrac, any potential effect of previous cross-linked treatment and the small slick water treatment prior to the main treatment is not considered. Furthermore, a difference between maximum and minimum horizontal stress is assumed to be 200 psi. Table 4 shows the main

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**Parameters Xlink Gel treatment Slick Water treatment**

parameters used for the fracture simulations.

Young's modulus 4.8 x 106 psi

Hydraulic fracture direction N40˚E Minimum horizontal stress 5324 psi Maximum horizontal stress 5524 psi

**Table 4.** Input data for Barnett Shale case

fracs at the end of the treatments.

Natural fracture direction Average N70˚W, standard deviation 5˚ Natural fracture length Average 200 ft, standard deviation 40 ft Natural fracture spacing Average 100 ft, standard deviation 20 ft Coefficient of friction Average 0.6, standard deviation 0.1

Fracture height 310 ft 360 ft Fluid rheology n' = 0.42, k' = 0.002 lb-s/ft<sup>2</sup> 1 cp Injection rate : Q 70 bpm 125 bpm Pump time 174 min 386 min Proppant volume 715,000 lbs 600,000 lbs

consistent with the microseismic observation shown in Figure 1a.

Figure 14 shows the UFM simulated fracture geometry and width for both gel and slick water

Planar hydraulic fractures first initiate from the perforations. These fractures propagate as longitudinal fractures since the wellbore direction is closely aligned with the fracture orien‐ tation. For the cross-linked gel treatment, as these initial longitudinal fractures intersect the natural fractures that are approximately orthogonal to the fracture direction, the OpenT crossing model mostly predicts crossing through the natural fractures. Only when the fluid pressure is sufficiently high to exceed the normal stress acting on the natural fractures, do the natural fractures be opened up and accept fracturing fluid. The overall geometry predicted by UFM model shows a strong planar trend along the well with very narrow network width,

Some of the critical information for fracture simulation, including Young's modulus and description of the natural fractures, is prescribed based on the work by Gale et al. [27]. According to [27], the Young's modulus for Barnett Shale is 33 GPa (4.8 x 106 psi). The natural fractures contain a dominant set trending West-Northwest direction (approximately North 70˚ West). There is also another set trending North-South direction. The hydraulic fractures in Barnett trend in the Northeast-Southwest direction. The natural fractures are mostly sealed and filled with calcite. Only largest fractures may be open and largest fracture clusters are expected to space couple hundred feet apart. To construct the natural fractures for UFM simulation, we assume that only the largest natural fractures contribute to the complex fracture network development. The exact values of fracture spacing and fracture length are difficult to determine. We make the assumption that the average fracture spacing is 100 ft and average fracture length is 200 ft. Only the dominant set of fractures is assumed. Figure 13 shows the top view of the well configuration, perforation clusters and the 2D traces of the generated natural fractures. The well geometry closely mimics the field case as shown in Figure 1.

**Figure 13.** Top view of the wellbore, perforations and the natural fractures used for the Barnett simulations

For the complex fracture simulation, detailed vertical stress profile is not available from [16]. Instead a fixed height model is used based on the microseismic measurements presented in [16]. It is assumed that the fracture height is 310 ft covering Lower Barnett for the case of crosslinked gel treatment, and 360 ft for the slick water treatment. For the simulation of slick water refrac, any potential effect of previous cross-linked treatment and the small slick water treatment prior to the main treatment is not considered. Furthermore, a difference between maximum and minimum horizontal stress is assumed to be 200 psi. Table 4 shows the main parameters used for the fracture simulations.


**Table 4.** Input data for Barnett Shale case

**4.3. Barnett example**

198 Effective and Sustainable Hydraulic Fracturing

formation configurations are very close to the real case.

To further validate the model in a realistic field condition, we examine a synthetic case that mimics the field example in Barnett Shale presented by Warpinski et al. [16] as shown in Figure 1. Though the details of the well and formation data and pumping schedule are not exactly replicated, the synthetic case is created using the data that is available in [16], so the well and

Some of the critical information for fracture simulation, including Young's modulus and description of the natural fractures, is prescribed based on the work by Gale et al. [27]. According to [27], the Young's modulus for Barnett Shale is 33 GPa (4.8 x 106 psi). The natural fractures contain a dominant set trending West-Northwest direction (approximately North 70˚ West). There is also another set trending North-South direction. The hydraulic fractures in Barnett trend in the Northeast-Southwest direction. The natural fractures are mostly sealed and filled with calcite. Only largest fractures may be open and largest fracture clusters are expected to space couple hundred feet apart. To construct the natural fractures for UFM simulation, we assume that only the largest natural fractures contribute to the complex fracture network development. The exact values of fracture spacing and fracture length are difficult to determine. We make the assumption that the average fracture spacing is 100 ft and average fracture length is 200 ft. Only the dominant set of fractures is assumed. Figure 13 shows the top view of the well configuration, perforation clusters and the 2D traces of the generated natural fractures. The well geometry closely mimics the field case as shown in Figure 1.

**Figure 13.** Top view of the wellbore, perforations and the natural fractures used for the Barnett simulations

Figure 14 shows the UFM simulated fracture geometry and width for both gel and slick water fracs at the end of the treatments.

Planar hydraulic fractures first initiate from the perforations. These fractures propagate as longitudinal fractures since the wellbore direction is closely aligned with the fracture orien‐ tation. For the cross-linked gel treatment, as these initial longitudinal fractures intersect the natural fractures that are approximately orthogonal to the fracture direction, the OpenT crossing model mostly predicts crossing through the natural fractures. Only when the fluid pressure is sufficiently high to exceed the normal stress acting on the natural fractures, do the natural fractures be opened up and accept fracturing fluid. The overall geometry predicted by UFM model shows a strong planar trend along the well with very narrow network width, consistent with the microseismic observation shown in Figure 1a.

**5. Possible impact on production forecast**

correctly predict hydraulic fracture complexity in naturally fractured formation.

**POSSIBLE IMPACT on PRODUCTION FORECAST** 

fracturing fluid viscosity and the proppant size

function of the fracturing fluid viscosity and the proppant size

of slick water and more viscous fluid pumped.

water and more viscous fluid pumped.

Figure1a.

[29] (Table 5).

understand the impact it could have on the production evaluation.

different fluids used, it is important to understand the impact it could have on the production evaluation.

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

Unconventional Fracture Model (UFM) presents a powerful tool to evaluate hydraulic fracture network propagation under the specified field pumping conditions and can be used to predict developed hydraulic fracture network and match it with observed microseismic event cloud. The proper understanding of the fracture footprint as well as propped fracture surface estimation is an important input for the production evaluation. Because the OpenT crossing model predicts some changes in crossing patterns with different fluids used, it is important to

For the slick water treatment, the OpenT crossing model mostly predicts non-crossing condition when a hydraulic fracture intercepts a natural fracture. This results in a much wider fracture network width as the fractures branch out as shown in Figure 14b. The width of the network is approximately 1700 ft wide, approximately the same as indicated by the microseismic data as shown in Figure 1b. Example presented on Figure 14 showing the difference in HFN from two treatments with different types of fluid, matches microseismic cloud trend observed in [16] and definitely shows ability of UFM simulator with new implemented crossing model

Planar hydraulic fractures first initiate from the perforations. These fractures propagate as longitudinal fractures since the wellbore direction is closely aligned with the fracture orientation. For the cross-linked gel treatment, as these initial longitudinal fractures intersect the natural fractures that are approximately orthogonal to the fracture direction, the OpenT crossing model mostly predicts crossing through the natural fractures. Only when the fluid pressure is sufficiently high to exceed the normal stress acting on the natural fractures, do the natural fractures be opened up and accept fracturing fluid. The overall geometry predicted by UFM model shows a strong planar trend along the well with very narrow network width, consistent with the microseismic observation shown in

Figure 14. UFM simulation results for the Barnett case

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

http://dx.doi.org/10.5772/56406<sup>201</sup>

This section illustrates how the crossing criterion can influence the production. It presents simulation results of a fracturing-to-production simulation. The production part is done with

This section illustrates how the crossing criterion can influence the production. It presents simulation results of a fracturing-toproduction simulation. The production part is done with the UPM model [28]. The base case for this Example 3 is from the paper

Unconventional Fracture Model (UFM) presents a powerful tool to evaluate hydraulic fracture network propagation under the specified field pumping conditions and can be used to predict developed hydraulic fracture network and match it with observed microseismic event cloud. The proper understanding of the fracture footprint as well as propped fracture surface estimation is an important input for the production evaluation. Because the OpenT crossing model predicts some changes in crossing patterns with

13

the UPM model [28]. The base case for this Example 3 is from the paper [29] (Table 5).

**NF friction coefficient=0 eRP criterion OpenT criterion**

80/100 40/70 30/50 20/40 80/100 40/70 30/50 20/40

Figure 15. Cumulated production after 3 years for the two crossing criterion and a friction coefficient at NF = 0, as a function of the

**Figure 15.** Cumulated production after 3 years for the two crossing criterion and a friction coefficient at NF = 0, as a

Figure15 shows the cumulated production after 3 years as a function of the proppant size and the fracturing fluid viscosity for the case when zero friction coefficient at NF is used and the two crossing models are applied. From Figure 15 we see that if there is no friction at the natural fractures, there is no difference between results from the two crossing criteria for this case. The reason is that if friction coefficient for the natural fracture is zero, both crossing criteria show that HF will not cross NF. The HFN footprint and fracture conductivity (identical when using both crossing criteria with zero friction coefficient) are shown at Figure 16 a,b for cases

Figure15 shows the cumulated production after 3 years as a function of the proppant size and the fracturing fluid viscosity for the case when zero friction coefficient at NF is used and the two crossing models are applied. From Figure 15 we see that if there is no friction at the natural fractures, there is no difference between results from the two crossing criteria for this case. The reason is that if friction coefficient for the natural fracture is zero, both crossing criteria show that HF will not cross NF. The HFN footprint and fracture conductivity (identical when using both crossing criteria with zero friction coefficient) are shown at Figure 16 a,b for cases of slick

(b)Slick water treatment

For the slick water treatment, the OpenT crossing model mostly predicts non-crossing condition when a hydraulic fracture intercepts a natural fracture. This results in a much wider fracture network width as the fractures branch out as shown in Figure 14b. The width of the network is approximately 1700 ft wide, approximately the same as indicated by the micro‐ seismic data as shown in Figure 1b.

Example presented on Figure 14 showing the difference in HFN from two treatments with different types of fluid, matches microseismic cloud trend observed in [16] and definitely shows ability of UFM simulator with new implemented crossing model correctly predict hydraulic fracture complexity in naturally fractured formation.

#### **5. Possible impact on production forecast** shows a strong planar trend along the well with very narrow network width, consistent with the microseismic observation shown in Figure1a.

[29] (Table 5).

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

(a)Cross-linked gel treatment

200 Effective and Sustainable Hydraulic Fracturing

(b)Slick water treatment

hydraulic fracture complexity in naturally fractured formation.

For the slick water treatment, the OpenT crossing model mostly predicts non-crossing condition when a hydraulic fracture intercepts a natural fracture. This results in a much wider fracture network width as the fractures branch out as shown in Figure 14b. The width of the network is approximately 1700 ft wide, approximately the same as indicated by the micro‐

Example presented on Figure 14 showing the difference in HFN from two treatments with different types of fluid, matches microseismic cloud trend observed in [16] and definitely shows ability of UFM simulator with new implemented crossing model correctly predict

**Figure 14.** UFM simulation results for the Barnett case

seismic data as shown in Figure 1b.

Unconventional Fracture Model (UFM) presents a powerful tool to evaluate hydraulic fracture network propagation under the specified field pumping conditions and can be used to predict developed hydraulic fracture network and match it with observed microseismic event cloud. The proper understanding of the fracture footprint as well as propped fracture surface estimation is an important input for the production evaluation. Because the OpenT crossing model predicts some changes in crossing patterns with different fluids used, it is important to understand the impact it could have on the production evaluation. For the slick water treatment, the OpenT crossing model mostly predicts non-crossing condition when a hydraulic fracture intercepts a natural fracture. This results in a much wider fracture network width as the fractures branch out as shown in Figure 14b. The width of the network is approximately 1700 ft wide, approximately the same as indicated by the microseismic data as shown in Figure 1b. Example presented on Figure 14 showing the difference in HFN from two treatments with different types of fluid, matches microseismic cloud trend observed in [16] and definitely shows ability of UFM simulator with new implemented crossing model correctly predict hydraulic fracture complexity in naturally fractured formation. **POSSIBLE IMPACT on PRODUCTION FORECAST**  Unconventional Fracture Model (UFM) presents a powerful tool to evaluate hydraulic fracture network propagation under the specified field pumping conditions and can be used to predict developed hydraulic fracture network and match it with observed

crossing through the natural fractures. Only when the fluid pressure is sufficiently high to exceed the normal stress acting on the natural fractures, do the natural fractures be opened up and accept fracturing fluid. The overall geometry predicted by UFM model

Figure 14. UFM simulation results for the Barnett case

This section illustrates how the crossing criterion can influence the production. It presents simulation results of a fracturing-to-production simulation. The production part is done with the UPM model [28]. The base case for this Example 3 is from the paper [29] (Table 5). microseismic event cloud. The proper understanding of the fracture footprint as well as propped fracture surface estimation is an important input for the production evaluation. Because the OpenT crossing model predicts some changes in crossing patterns with different fluids used, it is important to understand the impact it could have on the production evaluation. This section illustrates how the crossing criterion can influence the production. It presents simulation results of a fracturing-toproduction simulation. The production part is done with the UPM model [28]. The base case for this Example 3 is from the paper

Figure 15. Cumulated production after 3 years for the two crossing criterion and a friction coefficient at NF = 0, as a function of the fracturing fluid viscosity and the proppant size Figure15 shows the cumulated production after 3 years as a function of the proppant size and the fracturing fluid viscosity for the case when zero friction coefficient at NF is used and the two crossing models are applied. From Figure 15 we see that if there is no **Figure 15.** Cumulated production after 3 years for the two crossing criterion and a friction coefficient at NF = 0, as a function of the fracturing fluid viscosity and the proppant size

13 friction at the natural fractures, there is no difference between results from the two crossing criteria for this case. The reason is that if friction coefficient for the natural fracture is zero, both crossing criteria show that HF will not cross NF. The HFN footprint and fracture conductivity (identical when using both crossing criteria with zero friction coefficient) are shown at Figure 16 a,b for cases of slick water and more viscous fluid pumped. Figure15 shows the cumulated production after 3 years as a function of the proppant size and the fracturing fluid viscosity for the case when zero friction coefficient at NF is used and the two crossing models are applied. From Figure 15 we see that if there is no friction at the natural fractures, there is no difference between results from the two crossing criteria for this case. The reason is that if friction coefficient for the natural fracture is zero, both crossing criteria show that HF will not cross NF. The HFN footprint and fracture conductivity (identical when using both crossing criteria with zero friction coefficient) are shown at Figure 16 a,b for cases of slick water and more viscous fluid pumped.


**0 friction +30/50 mesh sand 1cP fluid: eRP and OpenT criteria 100 cP fluid: eRP and OpenT criteria** 

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

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**0 friction +30/50 mesh sand 1cP fluid: eRP and OpenT criteria 100 cP fluid: eRP and OpenT criteria** 

**Figure 16.** (a). HFN footprint for two types of fluid (with 30/50 mesh sand) for zero friction coefficient at NF. Both criteria show the same HFN footprint. (b). Fracture conductivity for two types of fluid (with 30/50 mesh sand) for zero

friction coefficient at NF. Both criteria show the same HFN footprint

(a)

(b)

**Table 5.** Input data for Example 3

If HF cannot cross NF, it is easier for slick water to penetrate and open NFs than for more viscous fluid, so HFN is generally more extended (Figure 16a left). While for the case of more viscous fluid it takes more time to open NF, HFN pattern is smaller (Figure 16)

When friction coefficient at NF is increased from 0 to 0.75, the output changes depending on the type of fluid pumped. The cumulated production forecast looks different for two crossing criteria used (Figure17).

For slick water treatments (Figure 18), eRP criterion shows some crossing of NFs (Figure18 left), while OpenT claims that no crossing should occur for slick water treatments (Figure 18 right). This difference in crossing models, produces considerable differences in production prediction after 3 years for low viscosity fluid treatments (Figure 17). It is important to mention, that it is a common observation that low viscosity fluids usually do not cross NFs, mainly because it is easier for them to penetrate to NF and open it [9]. The eRP criterion cannot capture this effect, while OpenT model correctly predicts HF/NF interaction for slick water case with friction coefficient at NF of 0.75.

When more viscous fluid pumped, results also show some differences (Figure19). With both criteria some crossing is observed, but OpenT in this case predicts more crossing than eRP criterion. Mention, that differences in results from eRP criterion for slick water and 100cP fluid are due to some differences in interaction angles between HF and NFs due to change in fluid properties, fluid pressure and due to stress shadow effect.

Due to small stress field anisotropy, the differences in production prediction for more viscous fluid are not significant (Figure 17), but still visible.

*Injection rate* 0.21 m3/s

Stress anisotropy 0.3 MPa Young's modulus 1.3×1010 Pa

Poisson's ratio 0.23

**Table 5.** Input data for Example 3

criteria used (Figure17).

friction coefficient at NF of 0.75.

properties, fluid pressure and due to stress shadow effect.

fluid are not significant (Figure 17), but still visible.

*Fluid viscosity* 0.001-0.1 Pa-s Min horizontal stress 28.47 MPa Max horizontal stress 28.76 MPa Fracture toughness 1.5 MPa-m0.5 Tensile strength 3.4MPa NF friction Coefficient 0 -0.75 NF permeability 1 Darcy

If HF cannot cross NF, it is easier for slick water to penetrate and open NFs than for more viscous fluid, so HFN is generally more extended (Figure 16a left). While for the case of more

When friction coefficient at NF is increased from 0 to 0.75, the output changes depending on the type of fluid pumped. The cumulated production forecast looks different for two crossing

For slick water treatments (Figure 18), eRP criterion shows some crossing of NFs (Figure18 left), while OpenT claims that no crossing should occur for slick water treatments (Figure 18 right). This difference in crossing models, produces considerable differences in production prediction after 3 years for low viscosity fluid treatments (Figure 17). It is important to mention, that it is a common observation that low viscosity fluids usually do not cross NFs, mainly because it is easier for them to penetrate to NF and open it [9]. The eRP criterion cannot capture this effect, while OpenT model correctly predicts HF/NF interaction for slick water case with

When more viscous fluid pumped, results also show some differences (Figure19). With both criteria some crossing is observed, but OpenT in this case predicts more crossing than eRP criterion. Mention, that differences in results from eRP criterion for slick water and 100cP fluid are due to some differences in interaction angles between HF and NFs due to change in fluid

Due to small stress field anisotropy, the differences in production prediction for more viscous

viscous fluid it takes more time to open NF, HFN pattern is smaller (Figure 16)

Number of Perforated Intervals 4

202 Effective and Sustainable Hydraulic Fracturing

**Figure 16.** (a). HFN footprint for two types of fluid (with 30/50 mesh sand) for zero friction coefficient at NF. Both criteria show the same HFN footprint. (b). Fracture conductivity for two types of fluid (with 30/50 mesh sand) for zero friction coefficient at NF. Both criteria show the same HFN footprint

the fracturing fluid viscosity and the proppant size

When friction coefficient at NF is increased from 0 to 0.75, the output changes depending on the type of fluid pumped. The

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

cumulated production forecast looks different for two crossing criteria used (Figure17).

open it [9]. The eRP criterion cannot capture this effect, while OpenT model correctly predicts HF/NF interaction for slick water case with friction coefficient at NF of 0.75. When more viscous fluid pumped, results also show some differences (Figure19). With both criteria some crossing is observed, but **Figure 17.** Cumulated production after 3 years for the two crossing criteria and a friction coefficient at NF of 0.75, as a function of the fracturing fluid viscosity and the proppant size

Figure 17. Cumulated production after 3 years for the two crossing criteria and a friction coefficient at NF of 0.75, as a function of

For slick water treatments (Figure 18), eRP criterion shows some crossing of NFs (Figure18 left), while OpenT claims that no crossing should occur for slick water treatments (Figure 18 right). This difference in crossing models, produces considerable differences in production prediction after 3 years for low viscosity fluid treatments (Figure 17). It is important to mention, that it is a common observation that low viscosity fluids usually do not cross NFs, mainly because it is easier for them to penetrate to NF and

OpenT in this case predicts more crossing than eRP criterion. Mention, that differences in results from eRP criterion for slick water and 100cP fluid are due to some differences in interaction angles between HF and NFs due to change in fluid properties, fluid pressure and due to stress shadow effect. **0.75 friction: 1cp+30/50 mesh sand eRP criterion OpenT criterion**  The main conclusion related to presented production examples, is that the difference between the two crossing criteria seems to be maximum for low viscosity fluid (slick water) and large proppant (30/50). This observation is expected because the lower the viscosity, the longer the fracture length and the stronger interaction with NF are. Also, eRP criterion shows some crossing of NFs for slick water case, while OpenT shows no crossing, and larger proppants are more sensitive to fracture intersections. The fracture width is larger if the HF does not cross NF and slurry propagated inside the NF with a larger normal stress (in case of stress aniso‐ tropy) and smaller width, thus increasing the likelihood of bridging. Also, the less crossing occurs, the more time HF needs to spend stopped at NF before building enough pressure to overcome the stress anisotropy and resume propagating inside the NF for high viscosity fluids. In this case, more proppant will settle close to the perforations, reducing the propped length and thus the production.

Figure 18a. HFN footprint for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75. Both criteria show similar HFN footprint. eRP shows some crossing (shown by dashed arrows), while OpenT shows no crossing for slick water case

15

(a)

(b)

**0.75 friction: 1cp+30/50 mesh sand eRP criterion OpenT criterion** 

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

http://dx.doi.org/10.5772/56406

205

**0.75 friction: 1cp+30/50 mesh sand eRP criterion OpenT criterion** 

**Figure 18.** (a). HFN footprint for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75. Both criteria show similar HFN footprint. eRP shows some crossing (shown by dashed arrows), while OpenT shows no crossing for slick water case (b). Fracture conductivity for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

cumulated production forecast looks different for two crossing criteria used (Figure17).

the fracturing fluid viscosity and the proppant size

204 Effective and Sustainable Hydraulic Fracturing

case with friction coefficient at NF of 0.75.

function of the fracturing fluid viscosity and the proppant size

pressure and due to stress shadow effect.

and thus the production.

When friction coefficient at NF is increased from 0 to 0.75, the output changes depending on the type of fluid pumped. The

**NF friction coefficient=0,75 eRP criterion OpenT criterion** 

80/100 40/70 30/50 20/40 80/100 40/70 30/50 20/40

Figure 17. Cumulated production after 3 years for the two crossing criteria and a friction coefficient at NF of 0.75, as a function of

For slick water treatments (Figure 18), eRP criterion shows some crossing of NFs (Figure18 left), while OpenT claims that no crossing should occur for slick water treatments (Figure 18 right). This difference in crossing models, produces considerable differences in production prediction after 3 years for low viscosity fluid treatments (Figure 17). It is important to mention, that it is a common observation that low viscosity fluids usually do not cross NFs, mainly because it is easier for them to penetrate to NF and open it [9]. The eRP criterion cannot capture this effect, while OpenT model correctly predicts HF/NF interaction for slick water

When more viscous fluid pumped, results also show some differences (Figure19). With both criteria some crossing is observed, but OpenT in this case predicts more crossing than eRP criterion. Mention, that differences in results from eRP criterion for slick water and 100cP fluid are due to some differences in interaction angles between HF and NFs due to change in fluid properties, fluid

**Figure 17.** Cumulated production after 3 years for the two crossing criteria and a friction coefficient at NF of 0.75, as a

The main conclusion related to presented production examples, is that the difference between the two crossing criteria seems to be maximum for low viscosity fluid (slick water) and large proppant (30/50). This observation is expected because the lower the viscosity, the longer the fracture length and the stronger interaction with NF are. Also, eRP criterion shows some crossing of NFs for slick water case, while OpenT shows no crossing, and larger proppants are more sensitive to fracture intersections. The fracture width is larger if the HF does not cross NF and slurry propagated inside the NF with a larger normal stress (in case of stress aniso‐ tropy) and smaller width, thus increasing the likelihood of bridging. Also, the less crossing occurs, the more time HF needs to spend stopped at NF before building enough pressure to overcome the stress anisotropy and resume propagating inside the NF for high viscosity fluids. In this case, more proppant will settle close to the perforations, reducing the propped length

**0.75 friction: 1cp+30/50 mesh sand eRP criterion OpenT criterion** 

Figure 18a. HFN footprint for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75. Both criteria show similar HFN footprint. eRP shows some crossing (shown by dashed arrows), while OpenT shows no crossing for slick water case

**Figure 18.** (a). HFN footprint for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75. Both criteria show similar HFN footprint. eRP shows some crossing (shown by dashed arrows), while OpenT shows no crossing for slick water case (b). Fracture conductivity for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75

15

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

**0.75 friction: 1cp+30/50 mesh sand eRP criterion OpenT criterion** 

Figure 18b. Fracture conductivity for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75

Due to small stress field anisotropy, the differences in production prediction for more viscous fluid are not significant (Figure 17), but still visible. **Figure 19.** (a). HFN footprint for 100cP viscosity fluid (with 30/50 mesh sand) for friction coefficient at NF=0.75. Ar‐ rows point at crossing (b). Fracture conductivity for 100cP viscosity fluid (with 30/50 mesh sand) for friction coefficient ant NF=0.75

Figure 19b. Fracture conductivity for 100cP viscosity fluid (with 30/50 mesh sand) for friction coefficient ant NF=0.75

16

print, could be different for low viscosity fluids and high viscosity fluids, while eRP model shows similar crossing patterns for low and high viscosity fluids. In the mean time both criteria

Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

It is important to mention that whether HF will cross (dilate, or open) NF depends on the combined impact of rock properties (local stress field, tensile strength, toughness, etc), NF properties (permeability, toughness, friction coefficient, cohesion, etc), HF/NF interaction

In general, the Unconventional Fracture Model (UFM) with new OpenT crossing model provides more reliable results to predict and evaluate hydraulic fracture network geometry and improve production forecast. The Barnett Shale example presented in Figure 14 shows the differences in HFN from two treatments with two different types of fluids. The predicted results closely match the microseismic cloud observed in [16] and show the ability of UFM simulator with the new crossing model (OpenT) to correctly predict hydraulic fracture

The authors would like to thank Schlumberger for permission to present and publish this paper

[1] Chuprakov DS, Akulich AV, Siebrits E, Thiercelin M. Hydraulic Fracture Propaga‐ tion in a Naturally Fractured reservoir. SPE 128715, Presented at the SPE Oil and Gas

[2] Zhao J, Chen M, Jin Y, Zhang G. Analysis of fracture propagation behavior and frac‐ ture geometry using tri-axial fracturing system in naturally fractured reservoirs. Int.

India Conference and Exhibition held in Mumbai, India, 20-22, January, 2010.

, Romain Prioul2

and Charles Cohen1

http://dx.doi.org/10.5772/56406

207

, Dimitry Chuprakov2

show similar results for some cases.

angle, fluid properties, injection rate and other properties.

complexity in naturally fractured formation.

**Acknowledgements**

**Author details**

**References**

Olga Kresse1\*, Xiaowei Weng1

1 Schlumberger, Sugar Land, USA

\*Address all correspondence to: okresse@slb.com

J. Rock Mech. & Min. Sci 2008; 45: 1143-1152.

2 Schlumberger Doll Research, Boston, USA

#### **6. Conclusions**

A new crossing model (OpenT) which takes into account fluid properties, properties of the rock mass and natural fractures, have been developed, validated [20, 21], and implemented in UFM. The similarities and differences in fracture footprint predicted based on the OpenT model and eRP criterion have been demonstrated and discussed. OpenT crossing model shows more realistic results for some cases (as for the field and laboratory observations) than existing purely rock property based models.

While eRP model properly accounts for interaction angle, stress anisotropy, rock tensile strength and NF friction coefficient, the OpenT model accounts also for NF and fluid proper‐ ties. The crossing prediction from OpenT criterion, and therefore corresponding HFN foot‐ print, could be different for low viscosity fluids and high viscosity fluids, while eRP model shows similar crossing patterns for low and high viscosity fluids. In the mean time both criteria show similar results for some cases.

It is important to mention that whether HF will cross (dilate, or open) NF depends on the combined impact of rock properties (local stress field, tensile strength, toughness, etc), NF properties (permeability, toughness, friction coefficient, cohesion, etc), HF/NF interaction angle, fluid properties, injection rate and other properties.

In general, the Unconventional Fracture Model (UFM) with new OpenT crossing model provides more reliable results to predict and evaluate hydraulic fracture network geometry and improve production forecast. The Barnett Shale example presented in Figure 14 shows the differences in HFN from two treatments with two different types of fluids. The predicted results closely match the microseismic cloud observed in [16] and show the ability of UFM simulator with the new crossing model (OpenT) to correctly predict hydraulic fracture complexity in naturally fractured formation.

## **Acknowledgements**

e International Conference for Effective and Sustainable Hydraulic Fracturing, 20-22 May 2013, Brisbane, Australia

**0.75 friction: 1cp+30/50 mesh sand eRP criterion OpenT criterion** 

Figure 18b. Fracture conductivity for slick water (with 30/50 mesh sand) for friction coefficient at NF=0.75

(a)

206 Effective and Sustainable Hydraulic Fracturing

(b)

but still visible.

**6. Conclusions**

purely rock property based models.

ant NF=0.75

**0.75 friction: 100cp+30/50 mesh sand eRP criterion OpenT criterion** 

**0.75 friction: 100cp+30/50 mesh sand eRP criterion OpenT criterion** 

Figure 19b. Fracture conductivity for 100cP viscosity fluid (with 30/50 mesh sand) for friction coefficient ant NF=0.75

Due to small stress field anisotropy, the differences in production prediction for more viscous fluid are not significant (Figure 17),

**Figure 19.** (a). HFN footprint for 100cP viscosity fluid (with 30/50 mesh sand) for friction coefficient at NF=0.75. Ar‐ rows point at crossing (b). Fracture conductivity for 100cP viscosity fluid (with 30/50 mesh sand) for friction coefficient

A new crossing model (OpenT) which takes into account fluid properties, properties of the rock mass and natural fractures, have been developed, validated [20, 21], and implemented in UFM. The similarities and differences in fracture footprint predicted based on the OpenT model and eRP criterion have been demonstrated and discussed. OpenT crossing model shows more realistic results for some cases (as for the field and laboratory observations) than existing

While eRP model properly accounts for interaction angle, stress anisotropy, rock tensile strength and NF friction coefficient, the OpenT model accounts also for NF and fluid proper‐ ties. The crossing prediction from OpenT criterion, and therefore corresponding HFN foot‐ The authors would like to thank Schlumberger for permission to present and publish this paper

## **Author details**

Olga Kresse1\*, Xiaowei Weng1 , Dimitry Chuprakov2 , Romain Prioul2 and Charles Cohen1

\*Address all correspondence to: okresse@slb.com

1 Schlumberger, Sugar Land, USA

2 Schlumberger Doll Research, Boston, USA

### **References**

16


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Effect of Flow Rate and Viscosity on Complex Fracture Development in UFM Model

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**Section 3**

**Regulations, Risks, and Communities**


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**Chapter 10**

**Hydrochemical and Hydrogeological Impact of**

**Hydraulic Fracturing in the Karoo, South Africa**

Hydraulic fracturing has become a prevalent public and regulatory issue in most countries developing shale gas. South Africa has only recently been exposed to terrestrial gas resource development and this has created unique regulatory issues which are currently being resolved. One of the key issues under debate is the protection of groundwater resources in rural areas, since most of South Africa's rural and some inland cities are dependent on groundwater for potable water supply. A second concern is the infrastructure requirements to handle the material movement processes during the development of each wellfield and subsequent processing of waste generated on site. Regarding the waste material production, a phased approach is required which considers the initial well development activities, production and subsequent well abandonment. Each phase has a unique risk associated with it and thus would require different management options. At the current stage most of the focus is on the initial stages of well development but the long term view has been neglected to some extent. Due to the unique geological structure of the Karoo, the presence of dolerite structures, a number of risk mitigation methods might be required to succesfully develop hydraulically fractured wells. In all aspects the chemical and hydrogeological impacts related to wellfield development cannot be ignored in the Karoo aquifer system, as it may directly influence human and environmental health. This paper will present chemical perspective on the hydraulic fracturing perspective that will deal with the impact of hydraulic fracturing fluid and flowback water. Additionally, the interaction of wellfield development and hydrogeology of the Karoo area

G. Steyl and G. J. van Tonder

http://dx.doi.org/10.5772/56310

**Abstract**

Additional information is available at the end of the chapter

will be discussed and how it relates to future water quality issues.

**Keywords:** Hydraulic fracturing, hydrochemistry, hydrogeology, impact

© 2013 Steyl and van Tonder; 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.
