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URTeC Control ID Number: 31743682
Evaluating the Effect of Natural Fractures on Production from
Hydraulically Fractured Wells Using Discrete Fracture Network
Models
Thomas Doe*
, Golder Associates Inc., Alfred Lacazette, Global Geophysical Services
Inc., William Dershowitz, and Clifford Knitter, Golder Associates Inc.
Copyright 2013, Unconventional Resources Technology Conference (URTeC)
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Denver, Colorado, USA, 12-14 August 2013.
The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper
have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is
subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not
necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper without the written consent of URTeC is prohibited.
Summary
Many unconventional reservoirs contain natural fractures. These fractures may be non-conductive but open
preferentially during hydraulic fracturing treatment, or they may be conductive prior to treatment and provide an
enlarged tributary drainage volume with different lateral extents than those suggested by conventional models of
unconventional reservoirs.
This paper presents a Discrete Fracture Network (DFN) study of gas production from an Eastern unconventional
reservoir that contains pre-existing, conductive fractures. The natural fractures are known through a combination of
innovative flow logging during drilling, image logging of the wells, and Tomographic Fracture Imaging™ (TFI).
Chemical frac-tracer monitoring confirms that a natural fracture network accesses a considerably larger volume of
rock than the microseismic data alone would indicate.
The results of these methods provide the basis for constructing a discrete fracture network model that honors the
conventional microseismic data, the flow logs, and the TFI fractures. Simulations of gas production from this
network model show that, although the major portion of production comes from the hydraulic fractures and nearby
closely-spaced natural fractures, the tributary drainage volume of the well extends well beyond the footprint of the
hydraulic fractures themselves.
Introduction
The role of natural fractures in unconventional reservoirs has been a topic of considerable discussion and
uncertainty. In some cases natural fractures may be non-conductive but may preferentially open and control the
propagation of hydraulic fractures. In other cases the natural fractures may be conductive and provide some
component of production in addition to hydraulically-stimulated artificial or natural fractures. The economic
development of unconventional resources involves efficient and relatively rapid drilling and completion strategies
that do not allow time for extensive characterization of the wells. Highly characterized wells that provide validation
for conceptual models of unconventional production are relatively uncommon. The Mallory 145 well pad is one of
these sites, and while the results should not be considered exemplary of unconventional reservoirs overall, they do
provide significant insights into how an unconventional system may behave. Some aspects of the Mallory 145
experiment are described in Mulkern et al, 2010; Franquet et al, 2011; Moos et al, 2011; Geiser et al, 2012;
Lacazette and Geiser, 2013; and Lacazette et al, 2013.
The Mallory 145 well site was developed by Pittsburgh-based EQT Corporation in Devonian shales and siltstones in
southwestern West Virginia. The well pad has four vertically-stacked laterals from the Berea sandstone, Chagrin
Shale, Lower Huron Siltstone and Lower Huron Shale. The Chagrin Shale well was used as a downhole

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microseismic monitoring well during hydraulic fracture treatments of the other three wells and was subsequently
fracture stimulated.
The development work involved several innovative methodologies for better understanding how these wells produce
gas from tight formations. First, all but the Berea well were air- drilled and fractured using pure nitrogen with no
proppant while the Berea well was fractured with 96% - 98% nitrogen foam and a small amount of proppant. The
fracture characterization work included
• extensive image logging,
• all of the wells were air-drilled and the return air was analyzed with a quadrupole mass spectrometer,
• conventional downhole microseismic monitoring of the fracturing,
• chemical tracing of the drilling gases and monitoring in nearby production wells, and
• Tomographic Fracture Imaging™ (TFI), which is a surface-based passive seismic monitoring method that
images a much larger volume of rock is covered by conventional downhole microseismic monitoring (Geiser
et al, 2006; Geiser et al, 2012; Lacazette and Geiser, 2013; Sicking et al 2013; Lacazette et al, 2013).
Taken as a whole the characterization activities identified a conducting fracture network that predated the well
stimulation activities. Although a major portion of the gas production likely comes from the surfaces of the
nitrogen-generated hydraulic fractures, the pre-existing conductive fractures provide an additional component of
production to the system.
Approach
This paper discusses the use of discrete fracture network (DFN) models to simulate gas production for the Mallory
145 pad from the induced and natural fracture systems. Although DFN models primarily involve flow only in
fracture networks, the production from the matrix can be simulated using either a complementary dual porosity
approach or by including layer-parallel fractures that have the same permeability and porosity as the matrix.
Discrete fracture network (DFN) models have seen increased use in recent years for both conventional and
unconventional reservoirs (Rogers, et al, 2010; Dershowitz, et al, 2011). DFN models represent conducting
fractures as discrete planar features in three dimensions with realistic geometries and reservoir properties. This
modeling approach is effective in capturing the heterogeneity and the variable connectivity of fracture networks as
well as providing realistic assessments of matrix block sizes for studies of fracture-matrix interaction. In recent
years extensions to DFN modeling approaches have included modules that simulate natural fracture opening due to
hydraulic fracture stimulation (Cottrell et al, 2013, this volume), thus helping to define the stimulated volumes of
unconventional reservoir treatment. DFN codes discretize the fracture network into elements or grid cells that
support flow and pressure calculations. This work uses Golder Associates’ FracMan™ DFN code with
modifications to calculate single phase gas flow.
The Mallory 145 DFN model has five major components:
• Hydraulic fractures,
• Natural fractures conditioned to the TFI data,
• Natural fractures conditioned to gas production from the quadrupole spectrometer logs,
• Stochastic natural fractures, and
• Single fractures representing the matrix.
The following sections describe the major data sources for defining these model components.
Fracture Data Sources: Flow Logging
As previously discussed, drilling and the wells were air-drilled and hydraulic fracturing for three of the wells was
done with nitrogen gas rather than with water while the Berea well was foam fraced. The return gases from drilling

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were analyzed by a quadrupole mass spectrometer produced by Fluid Inclusion Technologies of Tulsa, Oklahoma
and operated by King Canyon Buffalo mud logging services. The spectrometer scans from 1 – 120 amu every 90
seconds. Argon gas was injected into the drilling air to measure sample lag. Data presented here is lagged to ±2-3
ft. Figure 1 compares the results of the gas return monitoring with those of a conventional open-hole spinner log.
After filtering the spectrometer results for gas spikes associated with drilling operations (primarily connection gas),
the resulting profile not only matches the overall form of the production log, but it also provides a higher level of
detail on the locations of conducting natural fractures. A typical flow anomaly has an initial peak followed by a
slow decline, which should be expected from the transient rate that would be produced under the constant wellbore
pressures of drilling. Borehole images and both the spinner and the mass spectrometer logs show a highly
conductive fracture at about 6900 feet measured depth. The spectrometer log also shows at least four and possibly
five additional inflow points.
Figure 1: Comparison of spinner log and gas return percent from quadrupole spectrometry. Bars denote conductive fracture locations
0
5
10
15
20
25
30
351
10
3000 3500 4000 4500 5000 5500 6000 6500 7000 7500
PercentNatrualinDrillGasReturns
Spinner,rps
Depth, ft
Spinner
For the purposes of building a DFN model, we use the total gas rate prior to hydraulic fracturing and assign that rate
proportionally to the magnitude of the spectrometer log flow anomalies. Dividing this rate by the difference of the
initial reservoir pressure and the wellbore pressure during drilling provides a PI for each conducting fracture.
Adjusting for the gas properties this PI provides an individual fracture approximate kh (or permeability aperture
product).
After hydraulic fracturing, a second set of spinner logs provided flow profiles for the wells. The hydraulic
fracturing was performed by the sliding sleeve method; hence the spinner anomalies represent the flow from a
section of hole rather than from discrete fractures. While the total flow rates before and after hydraulic fracturing
treatment are similar, a comparison of the interval rates before and after hydraulic fracturing shows that the
treatments redistributed the flow among different sections of the well. This redistribution suggests that the hydraulic
fracturing created more efficient pathways into the well for the natural fractures.
Data Sources: Conventional Microseismic Data
Conventional microseismic were collected for three of the laterals using the fourth lateral as a location for
microseismic instrumentation. Figure 2 shows the seismic data for the Berea lateral, which is typical of all of the
wells monitored. The micro-earthquake data are strongly aligned with the northeast-trending maximum horizontal
stress as well as one of the main natural fracture orientations. The fracturing gases may have moved in the
maximum horizontal stress direction by laddering through the pre-existing natural fracture network. The half
lengths of the hydraulic fractures in the stimulated region are less than 300 m (~1000 ft).

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Data Sources: Tomographic Fracture Imaging™ (TFI)
Tomographic Fracture Imaging, or TFI, is a method of three-dimensional mapping of cumulative seismic energy
releases during hydraulic fracturing treatments. TFI is a passive microseismic method that uses surface arrays for
imaging both natural and induced fractures at the reservoir scale. Total trace energy is summed over periods
ranging from minutes to hours. Clipping of the volumes yields high energy clouds. The central surfaces of those
clouds are the large fracture surfaces (Geiser et al, 2006; Geiser et al, 2012; Lacazette and Geiser, 2013; Sicking et
al 2013; Lacazette et al, 2013).
Figure 2. Conventional microseismic data from Berea lateral. Reference lines are 1 kilometer apart (3280 feet).
Figure 3. Tomographic Fracture Images™ (TFI) taken during hydraulic fracturing in the Berea Sandstone shown in black. Surface topography
shown in rainbow colors (blue is low). Upper left is data for all stages. Other figures show single stage results. Note well and conventional
microseismic (red) locations. Box scale is 3 miles (4900 m). TFIs shown as 3D surfaces. See depth slices in Geiser et al (2012) and Lacazette et
al (2013) for comparison.
Berea
Stage 4
Berea
Stage 5
Berea
Stage 6

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Data Sources: Tracers
Tracers in the drilling and the fracturing gases provided a means of assessing connectivity between the laterals of the
Malory 145 pad and also between the pad and nearby production wells (Mulkern et al, 2010; Geiser et al, 2012).
The tracers showed connectivity both between the laterals of the pad and also to production wells 1 to 1.5 kilometers
(3500-5000 feet) away from the Mallory 145 pad in regions that correspond to the Southwest and Northeast TFI
clusters. The tracers confirm the connectivity of fracture networks beyond the footprint of the conventional
microseismic results.
Figure 4. Tracer responses between Mallory 145 and nearby wells.
3 miles
Data Sources: Fracture Image Logging and Borehole Stress
The characterization efforts included an extensive program of fracture image logging. The image logs show two
dominant NE-SW and SW-NE trending joint sets and a less well developed conjugate pair of NE-SW trending
extensional faults. Some additional weakly developed joint sets are present (Figure 5; Lacazette et al, in prep).
Borehole images, sonic logs, and other observations show that the area is in a normal faulting stress state, that the
horizontal maximum stress trends NE-SW, and that the two horizontal stresses have similar magnitudes (Moos et al,
2011).
Figure 5. Cartoon of the fracture system at the Mallory 145 pad based 2351 observations of natural fractures in borehole images from four wells.

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DFN Model Construction
The DFN model has five major components shown in Figure 6:
• Hydraulic fractures,
• Deterministic natural fractures conditioned to the TFI data,
• Natural fractures conditioned to the quadrupole spectrometer logs,
• Stochastic natural fractures based on the TFI data, and
• A single horizontal fracture to represent the matrix.
The hydraulic fractures are deterministic features that are fitted to the conventional microseismic mapping. While
these fractures are likely to be relatively complex, we are simulating each stage’s hydraulic fracture as a single
fractures with a height and length corresponding to the locations of the microseismic activity. They appear in Figure
6 as yellow features.
All of the natural fractures are based on the TFI data, but they are generated one of three ways depending on
• Whether or not they intersect the well,
• Whether or not they appear as deterministic fractures in the TFI data, or
• Whether they are filling the volume of the model outside the space where the TFI identified fractures.
The natural fractures that intersect with the well are conditioned to the quadrupole mass spectrometer flow
anomalies. The locations of these fractures are based on these well intersections. They are given the hydraulic
properties inferred from the measured flow rates and they are given random lengths and orientations that are
sampled from the TFI fractures. The fractures that are conditioned to the well appear as blue features in Figure 6.
The term “deterministic” with reference to fractures means the fractures have known locations and orientations.
These are fractures that are mapped directly from the TFI data. They are randomly given hydraulic properties based
on the kh distribution of the flow log fractures. The deterministic TFI fractures appear mainly near the well with
extensions into the three TFI clusters of Figure 3. The deterministic fractures are light green in Figure 3.
Figure 6. DFN model elements.
•Hydrofracs (yellow)
•Conditioned Fractures in Well (blue)
•Deterministic TFI Fractures (light green)
•Stochastic TFI Fractures (dark green)

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The rest of the model region is assumed to contain natural fractures like those that appear in the TFI data. They do
not appear in the TFI data because they are either not connected to the source wells or they are too far away for fluid
pressure to have diffused into them to produce seismic responses. The locations and orientations of these fractures
are not known, but we assume that they are statistically similar to the ones we observe in the TFI data.
We generate this as a stochastic set of fractures with the same orientation, size, and intensity statistics as the
deterministic TFI fractures. We exclude the stochastic fractures from occupying the same space as the deterministic
fractures by superposing a grid on the entire model. We calculate the intensity of deterministic fractures in each grid
cell. The intensity values we use for generating the stochastic set are inversely conditioned in each grid cell to the
intensity of the deterministic set. This means that a stochastic fracture has a low probability of being generated in a
cell that already has deterministic fractures.
Fracture network models explicitly represent only the fractures in a reservoir volume. Although it is possible to
discretize the matrix between the fractures, this usually increases the computational burden to the extent that the
network can only contain a few fractures. To include the matrix while maintaining computational efficiency, we use
one of two different methods – DFN dual porosity and a matrix fracture. DFN dual porosity associates a one-
dimensional flow solution with each finite element of each fracture. This approach is effective as long as the matrix
permeability is sufficiently low that matrix flow occurs from points in the matrix to the nearest fracture and not
across matrix blocks between fractures. The parameters for DFN dual porosity include the matrix permeability,
porosity, and compressibility as well as the depth of simulation into the matrix blocks and the number of elements
used for the matrix one-dimension simulation. The spacing of these elements is logarithmic; hence the elements
closest to the fracture may have very small spacings, which improve the accuracy of the numerical simulation when
there is a large contrast of permeability between the fractures and the matrix. The major disadvantage of the DFN
dual porosity approach arises in complex networks. As the matrix solution uses a fixed depth, it does not account
for the possibility of overlapping matrix volumes.
The second approach to simulating matrix is the use of a matrix fracture. A matrix fracture is a horizontal or
bedding-parallel fracture that has the permeability and storage properties of the matrix. This approach works best
when the flow is primarily confined to a single layer and there is not a significant component of flow between
layers; however, it is possible to use multiple matrix fractures to represent different layers. As fractures have 100%
porosity, the aperture of the matrix fracture is the layer thickness times the porosity of the matrix in the layer. In
order to preserve the kh of the matrix, the permeability of the equivalent matrix fracture should be divided by the
porosity. For example, a matrix layer with a porosity of 10% and a thickness of 10 m will have an aperture of 1 m.
If the matrix has a permeability of one µdarcy, the matrix fracture will have a permeability of 10 µd. The main
advantage of a matrix fracture is that the simulations accurately represent both the heterogeneity of the block sizes
and the fracture permeabilities. The main disadvantage of the matrix fracture lies in the inability to discretize it
finely enough in the area of the fracture intersections without greatly increasing the total number of elements in the
simulation.
DFN Model Simulations
The discrete fracture network model described above was used to simulate gas production from two layers of the
model, the Berea Sandstone and the Lower Huron Shale. The Berea Sandstone was modeled with a one µdarcy
permeability and the shale was modeled with a 10 nanodarcy permeability. Both were simulated with 5% porosity
values. The FracMan model was 3 miles on a side and the simulations were run for 350 days of production. The
FracMan flow simulator is single phase only; however the simulations were run using in situ gas densities and
viscosities, and the flow rates were adjusted for gas flow using a pseudo pressure approach. Previous simulations
assuming classic hydraulic fractures showed that the production data could be matched approximately using
analytical solutions for constant-pressure production of gas (el Banbi, 1998). The same analytical solutions were
used for validating the gas flow approach in FracMan.
The fracture network model is also capable of approximately matching the production data with the additional
tributary drainage volume of the natural fractures. Figure 7 shows the pressure drawdowns for one of the wells over
350 days. The visualization of pressure drawdowns shows gas depletion from the volumes between hydraulic
fractures and from volumes where natural fractures are closely spaced. That said, the pressure influence of

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production extends for distances of over approximately 1.5 km or one mile from the producing wells. These
distances of influence in the model are consistent with the tracer recoveries in other production wells.
Figure 8 shows the simulated and actual cumulative production for one of the laterals. The three solid lines
represent simulations using all three types of natural fractures with or without the hydraulic fractures and the matrix.
The three cases shown are:
• Natural fractures with no matrix interaction –neither DFN dual porosity nor matrix fracture
• Natural fractures with DFN dual porosity matrix, and
• Hydraulic fractures and natural fractures with DFN dual porosity matrix.
Figure 7. Visualization of pressure drawdown in a 3-mile region including the Berea matrix represented as a fracture.
Drawdown,
psi
Figure 8. Measured and simulation cumulative production.
LH Silt Production
Fractures Only
Dual Porosity
Fractures and
Hydrofracs Dual
Porosity
Fracture No
Matrix
h=117ft.
Time
Production
CumulativeProduction

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The production from natural fractures alone is relatively small, which is not surprising as natural fractures have little
storage and depend on gas sources in the matrix to support pressure and flow. The addition of matrix to the natural
fractures increases the production, but to only about a third of the magnitudes of the hydraulic fractures, natural
fractures, and matrix together. These results suggest that the major portion of production is coming primarily from
the matrix between the hydraulic fractures and secondarily from the matrix accessed only by the natural fractures.
Conclusions
A combination of fracture imaging, quadrupole mass spectrometer flow logs, Tomographic Fracture Imaging, and
tracer tests shows that natural, conductive fracture influence production from an Eastern US unconventional gas
reservoir. The natural fractures create a significantly larger tributary drainage volume than one would expect from
only the hydraulic fractures. The measurements support building a discrete fracture network model that honors the
conductive fractures intersecting the wells and the fractures mapped by the Tomographic Fracture Images. Flow
simulations using this DFN model show that a major portion of the production nonetheless comes from the hydraulic
fractures and a secondary portion come from the natural fractures and the matrix they access at a distance from the
well.
The possibility of significant contributions to production from natural fractures that extend well outside the
hydrofracture footprint goes beyond current paradigms of production from unconventional reservoirs. Although this
case may not be representative of all unconventional reservoirs, this unusually well-characterized example provides
a useful example of how natural fractures may affect production. The Tomographic Fracture Imaging is an
effective means for identifying natural fracture sources that extend beyond the hydrofracture footprint. The DFN
modeling approach provides a method for assessing the production implications of the TFI information.
Acknowledgements
The authors acknowledge the contributions of our Golder colleagues, Doo-Hyun Lim and Todd Hoffman (currently
Colorado School of Mines). We particularly thank EQT for providing us the opportunity to work on this unique and
valuable data set. Our co-author, Dr. Lacazette, conceived of this overall study and managed it while on the staff of
EQT. He deserves the main credit for its overall success. The Golder co-authors are grateful to him for involving us
in this work.
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