spe urtec eqt shale gas 2013

URTeC Control ID 31743682

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


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


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

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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).


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

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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.

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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.


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)


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


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.

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Figure 8. Measured and simulation cumulative production.

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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. References Cottrell, M., H. Hosseinpour, and W. Dershowitz, Rapid discrete fracture analysis of hydraulic fracture development in naturally fractured reservoirs, Unconventional Resources Technology Conference, URTeC 1582243, 2013 (this volume) Dershowitz W.S., R. Ambrose., D.-H. Lim., and M.G. Cottrell, Hydraulic fracture and natural fracture simulation for improved shale gas development. American Association of Petroleum Geologists (AAPG) Annual Conference and Exhibition Houston, 2011 El-Banbi, A. H., Analysis of Tight Gas Well Performance, PhD dissertation Texas A&M University, 196 p., 1998 Franquet, J., A. Mitra, D.S. Warrington, D. Moos, and A. Lacazette, Integrated acoustic, mineralogy, and geomechanics characterization of the Huron Shale, Southern West Virginia, USA: SPE 148411, 2011 Geiser, P.A., Vermilye, J., Scammell, R., Roecker, S., Seismic used to directly map reservoir permeability fields. Oil Gas Journal,. v. 104, Issue 46, Dec., 2006 Geiser, P., Lacazette, A., Vermilye, J., Beyond “dots in a box”: An empirical view of reservoir permeability with tomographic fracture imaging. First Break, v. 30, p. 63-69, 2012 Lacazette, A., Geiser, P., Comment on Davies et al., 2012, Hydraulic fractures: How far can they go? Marine and Petroleum Geology v. 43, 516-518. 2013


Lacazette, A., Vermilye, J., Fereja, S., Sicking, C., Ambient fracture imaging: a new passive seismic method: URTeC 1582380, 2013 (this volume) Moos, D., G. Vassilellis, R. Cade, J. Franquet, A. Lacazette, E. Bourtembourg, and G. Daniel, Predicting shale reservoir response to stimulation: the Mallory 145 Multi-Well Project: SPE 145849, 2011 Mulkern, M., M. Asadi, and S. McCallum, Fracture extent and zonal communication evaluation using chemical gas tracers, SPE 138877, 2010 Rogers S.F., Elmo, D., Dunphy, R., and Bearinger, D., Understanding hydraulic fracture geometry and interactions in the Horn River Basin through DFN and numerical modelling, Canadian Unconventional Resources & International Petroleum Conference held in Calgary, Alberta, Canada, 19–21, October 2010 Sicking, C., Vermilye, J., Geiser, P., Lacazette, A., Permeability field imaging from microseismic. Geophysical Society of Houston Journal, v. 3, p. 11-13, 2013

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