**6. Hydraulic fracturing design and reservoir simulation**

As discussed earlier, a commercial simulator was used to model the hydraulic fracture created during the stimulation along with the stimulated natural fracture network using low-viscosity fluids. Stress profiles and other elastic rock properties estimated in the geomechanical analysis were used as input for the design. To achieve better proppant distribution, a low-viscosity linear gel was combined with slickwater in the treatment. The low-viscosity linear gel was optimized using different concentrations of ingredients for the high reservoir temperature (~126°C) using source water and local ingredients. Due to the high closure pressure and low viscosity of the fluid, high-strength small-mesh proppants were used in the design.

Modeling showed that four stages would be required for slickwater/linear gel treatment to cover the 160 m thick reservoir due to the high leak off of low-viscosity fluids (Figure 13). A

**Figure 12.** Side view (left) and top view (right) of the predicted 3D permeability map. The property shown in the plots

**Figure 11.** Relationship between the permeability multiplier and the pore pressure (green curve) for natural fractures used in the simulation. Three different flow paths (A, B, C) were assumed for conditions with pressure above Shmin.

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reasonable proppant distribution was achieved by using the low-viscosity linear gel.

is present fracture permeability.

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described in the previous section, it is better to calibrate and constrain the permeabilitypressure relationship based on real lab or in-situ tests, e.g., using a pre-stimulation injectivity test [4]. The injectivity test should ideally be conducted in the open hole using slow injection to evaluate the potential natural fractures being stimulated, as permeability changes could then be interpreted based on the flow-rate/pressure changes along with the reservoir pressure. Because the D3 well has already been cased it was impossible to conduct such a test in the field before the actual treatment is carried out. Consequently, it was decided to produce a perme‐ ability-pressure table based on experience from shale gas reservoirs. Based on this table, on fracture density in different directions and on the stress anisotropy, a composite transmissi‐ bility multiplier was produced for the prediction of properties and extent of the stimulated reservoir volume. Transmissibility multipliers were different for each of the I, J and K direc‐ tions; those directions were aligned as discussed above with the primary structural fabric and stresses. The propagation of the pressure and fluid front in these directions can be controlled

Figure 11 shows diagrammatically the relationship between the permeability multiplier and the pore pressure (green curve). A slow increase in the permeability multiplier with increasing pressure occurs until fractures begin to slip. Above this pressure, the injectivity increases rapidly as an increased number of fractures are stimulated. During decreasing injection pressure in the injectivity test, the injectivity should decrease more slowly, retaining behind a permanent injectivity increase. The post-stimulation response can also be extrapolated to pressures below the original reservoir pressure. This makes it possible to predict the reservoir's response to depletion, which could lead to improved predictions of production decline. When the pressure during stimulation exceeds the minimum horizontal stress, extensional hydro‐ fracs are created, and the permeability-pressure relationship does not follow the green line. Three different flow paths (A, B, C) were assumed for conditions with pressure above Shmin,

The result of this modeling work is a 3D induced permeability map that describes the stimu‐ lated rock volume as discrete blocks, each with a unique permeability. The stimulated rock volume is therefore described not as a geometrical shape with identical flow properties throughout, but as a rock body with variable induced permeability, as shown in Figure 12.

As discussed earlier, a commercial simulator was used to model the hydraulic fracture created during the stimulation along with the stimulated natural fracture network using low-viscosity fluids. Stress profiles and other elastic rock properties estimated in the geomechanical analysis were used as input for the design. To achieve better proppant distribution, a low-viscosity linear gel was combined with slickwater in the treatment. The low-viscosity linear gel was optimized using different concentrations of ingredients for the high reservoir temperature (~126°C) using source water and local ingredients. Due to the high closure pressure and low

viscosity of the fluid, high-strength small-mesh proppants were used in the design.

and the intermediate path, B was chosen to be used in the simulation.

**6. Hydraulic fracturing design and reservoir simulation**

by modifying these multipliers.

1030 Effective and Sustainable Hydraulic Fracturing

**Figure 11.** Relationship between the permeability multiplier and the pore pressure (green curve) for natural fractures used in the simulation. Three different flow paths (A, B, C) were assumed for conditions with pressure above Shmin.

**Figure 12.** Side view (left) and top view (right) of the predicted 3D permeability map. The property shown in the plots is present fracture permeability.

Modeling showed that four stages would be required for slickwater/linear gel treatment to cover the 160 m thick reservoir due to the high leak off of low-viscosity fluids (Figure 13). A reasonable proppant distribution was achieved by using the low-viscosity linear gel.

To compare the prediction result from slickwater/liner gel treatment with conventional gel fracturing, a conventional bi-wing hydraulic fracturing design using a high-viscosity gel was also developed. The gel fluid was optimized using different concentrations of ingredients for the high reservoir temperature (~126°C) using source water and local ingredients. The same type of proppant used for the slickwater/liner gel treatment was used for the design of gel treatment. The proppant concentrations and amounts will be certainly different in these two types of treatments. It was found that two stages were enough to cover the whole reservoir interval (Figure 15). These two designed hydraulic fractures were then imported into the original reservoir model (right plot in Figure 14) for production prediction and comparison of

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(a) (b)

**Figure 14.** Side views of reservoir models showing fracture permeability used for production prediction. (a) Reservoir model combining four Model A's with stimulated reservoir volumes and four propped hydraulic fractures using slick‐ water and linear gel; (b) Original reservoir model and two propped hydraulic fractures using high-viscosity gel fluid.

Figure 16 shows the production prediction comparison from the two different hydraulic fracturing treatments. The red curve is the production prediction from slickwater/linear gel treatment, which is scaled down to ~2/3 of the initial prediction to account for the heterogeneity of the reservoir model due to a simplified reservoir model used for pre-stimulation condition. The blue curve is the production from conventional two-wing gel fracturing design. It is found that post-frac flow rate from slickwater stimulation is expected to be about three times the flow rate from the gel treatment in the stabilized regime (one year after stimulation). Although actual flow rates from both treatments depends on the applied drawdown, the corresponding

/d for gel treatment, respectively, with a constant drawdown of 20 MPa.

m3

/d for slickwater treatment and ~ 17 ×

flow rates after one year are expected to be ~55 × 104

104 m3

the production to that predicted after slickwater linear gel stimulation.

**Figure 13.** Fracture growth and proppant coverage (colour) for four stage of hydraulic fractures using slickwater/line‐ ar gel.

To predict the production after the stimulation, the propped hydraulic fractures were imported into the reservoir model with flow properties enhanced by stimulated natural fractures (Model A). Because the natural fracture distribution between wells is unknown, the same stimulated Model A was used for all four stages. The left plot of Figure 14 shows a side view of the reservoir model combining four Model A's with stimulated reservoir volumes and four propped hydraulic fractures, which was used for production prediction.

To compare the prediction result from slickwater/liner gel treatment with conventional gel fracturing, a conventional bi-wing hydraulic fracturing design using a high-viscosity gel was also developed. The gel fluid was optimized using different concentrations of ingredients for the high reservoir temperature (~126°C) using source water and local ingredients. The same type of proppant used for the slickwater/liner gel treatment was used for the design of gel treatment. The proppant concentrations and amounts will be certainly different in these two types of treatments. It was found that two stages were enough to cover the whole reservoir interval (Figure 15). These two designed hydraulic fractures were then imported into the original reservoir model (right plot in Figure 14) for production prediction and comparison of the production to that predicted after slickwater linear gel stimulation.

**Figure 14.** Side views of reservoir models showing fracture permeability used for production prediction. (a) Reservoir model combining four Model A's with stimulated reservoir volumes and four propped hydraulic fractures using slick‐ water and linear gel; (b) Original reservoir model and two propped hydraulic fractures using high-viscosity gel fluid.

Figure 16 shows the production prediction comparison from the two different hydraulic fracturing treatments. The red curve is the production prediction from slickwater/linear gel treatment, which is scaled down to ~2/3 of the initial prediction to account for the heterogeneity of the reservoir model due to a simplified reservoir model used for pre-stimulation condition. The blue curve is the production from conventional two-wing gel fracturing design. It is found that post-frac flow rate from slickwater stimulation is expected to be about three times the flow rate from the gel treatment in the stabilized regime (one year after stimulation). Although actual flow rates from both treatments depends on the applied drawdown, the corresponding flow rates after one year are expected to be ~55 × 104 m3 /d for slickwater treatment and ~ 17 × 104 m3 /d for gel treatment, respectively, with a constant drawdown of 20 MPa.

**Figure 13.** Fracture growth and proppant coverage (colour) for four stage of hydraulic fractures using slickwater/line‐

To predict the production after the stimulation, the propped hydraulic fractures were imported into the reservoir model with flow properties enhanced by stimulated natural fractures (Model A). Because the natural fracture distribution between wells is unknown, the same stimulated Model A was used for all four stages. The left plot of Figure 14 shows a side view of the reservoir model combining four Model A's with stimulated reservoir volumes and four propped

hydraulic fractures, which was used for production prediction.

ar gel.

1032 Effective and Sustainable Hydraulic Fracturing

**7. Injectivity test and stage 1 treatment**

perforated hole and after a mini-frac.

**8. Discussion and conclusion**

fixed.

It was decided to test the slickwater/liner gel treatment in D3 well after the study was com‐ pleted. A pre-stimulation injectivity test was performed through perforations prior to Stage 1 and after the mini-frac test (Figure 17). Interestingly, the test showed the opposite behavior from what one would expect if the stimulation enhances reservoir permeability. Later-stage injectivity (during step-down) is lower than early stage injectivity (during step-up), rather than higher. Although there might be other reasons affect the test result, i.e., the un-stable injection during the whole test, it is believed the main reason was lack of access to natural fractures in the tested interval and the high closure pressure because the test was conducted in a cased and

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(a) (b)

The Stage 1 treatment was conducted using slickwater and linear gel after the injectivity test. However, a screen out was experienced at the end of the execution and tubing leakage was discovered afterwards. Treatments in the other three zones had not occurred at the date of writing this paper. The stage 1 production test is still very promising, and it has been decided to continue slickwater/linear gel treatment in other three stages after the tubing problem is

In this paper we have outlined a new workflow for simulation of a complex fracture network created by stimulation using low-viscosity fluids in a fractured tight sandstone reservoir. The workflow is based on critically stressed fracture theory. This process of natural fracture stimulation is believed to be the underlying reason for the success in shale gas reservoir stimulation. The results suggested that there would be significantly higher production from

**Figure 17.** Pre-stimulation injectivity test pressure curve (a) and injectivity interpretation (b).

this approach compared to conventional two-wing gel fracturing.

**Figure 15.** Fracture growth and proppant coverage (colour) for two stage of hydraulic fractures using conventional gel treatment.

**Figure 16.** Production prediction comparison of two different hydraulic fracturing treatments. The red curve is the production prediction from slickwater/linear gel treatment; the blue curve is the production from conventional twowing gel fracturing design.
