SPE 133877

Hydraulic Fracture Monitoring to Reservoir Simulation: Maximizing Value C.L. Cipolla, M.J. Williams, X. Weng, M. Mack, and S. Maxwell, Schlumberger

Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in Florence, Italy, 19–22 September 2010. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract Hydraulic fracture monitoring with microseismic mapping is now routinely used to measure hydraulic fracture geometry, location, and complexity, providing an abundance of information that can be essential to optimizing stimulation treatments and well completions. Although microseismic mapping has added significant value in many different environments, we have yet to fully utilize microseismic data. Significant details can be extracted from microseismic measurements that, when integrated with other information, can improve the characterization of both the reservoir and the hydraulic fracture. In addition, microseismic data has yet to be quantitatively and routinely utilized in reservoir simulation, which is the key to optimization.

Geological and geophysical data and wellbore logs can be combined with newly-developed complex fracture propagation models and reservoir simulation models. These models are calibrated using microseismic measurements and production data—closing the loop from microseismic mapping to simulation. The combination of microseismic measurements and complex fracture modeling with sophisticated geological descriptions of pre-existing natural fractures can be used to evaluate existing and predict future well performance in complex shale-gas reservoirs. The application of calibrated complex hydraulic fracture and reservoir simulation models provides more reliable forecasts of well performance resulting from various hydraulic fracture designs and completion scenarios, allowing the selection of the most economic strategy.

The optimization process includes a detailed workflow to efficiently integrate the large amount of information and modeling results into a coherent work product. This includes the integration of advanced processing and geomechanical interpretations of microseismic data with newly developed complex hydraulic fracture models that significantly improve the application of microseismic measurements. An example illustrates how HFM can be taken from event locations to production forecasts, showing how the capability to integrate geophysics, geomechanics, hydraulic fracture mechanics, and reservoir simulation can result in significant economic benefits.

Introduction The introduction of microseismic hydraulic fracture mapping (MSM) has added immensely to our understanding of fracture propagation, especially in unconventional reservoirs such as shale-gas (Fisher et al. 2002, Maxwell et al. 2002, Daniels et al. 2007, Le Calvez et al. 2007, King et.al. 2008, Warpinski et al. 2008, Vincent 2009, and Waters et al. 2009). However, this added information has also increased our uncertainty. The complexity and diversity of hydraulic fracture propagation that has been measured using MSM has resulted in a significant void in our current workflows. To fully exploit MSM measurements, three significant advances were required – the development of hydraulic fracture propagation models that can predict fracture growth in unconventional reservoirs, methods to extract complex fracture structures from microseismic measurements, and the efficient integration of numerous geological and geophysical (G&G) measurements with complex fracture modeling, geomechanics, and reservoir simulation. Recent advances in complex fracture modeling (Xu et al. 2010, Meyer 2009) have provided a means to evaluate and predict fracture propagation in unconventional reservoirs, enabling a workflow that has been critically absent from unconventional reservoir development, namely microseismic hydraulic fracture monitoring to reservoir simulation. In the past reservoir simulation workflows have focused on full-field modeling and conventional reservoirs. As more and more unconventional reservoirs have been developed it has become clear that reservoir simulation workflows must be adapted to address the completion and stimulation strategies that are critical to unconventional reservoirs. These reservoir simulation workflows must be focused on single well modeling to capture the details of the completion and the stimulation (hydraulic fracture). Integrating

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planar hydraulic fracture models with single-well reservoir simulation has been sporadically applied for decades, but these workflows are frequently inadequate for unconventional reservoirs, as they do not properly describe the details of hydraulic fracture growth and production mechanisms in these complex environments. In unconventional reservoirs, understanding the interaction of the hydraulic fracture with pre-existing natural fractures and rock fabric is a crucial component of stimulation design and production evaluation/forecasting.

In addition to complex fracture modeling, advances have been made in the interpretation of microseismic measurements that provide a method to extract hydraulic fracture planes from microseismic data and understand fracture orientations (Williams et al. 2010, Baig and Urbancic 2010, Maxwell et al. 2007). These methods can be used independently or in conjunction with complex hydraulic fracture modeling.

The ultimate goal of hydraulic fracture monitoring is to completely characterize the hydraulic fracture structure and distribution of conductivity within this structure. Unfortunately we are still very far from achieving this lofty goal, due to limitations in our ability to effectively acquire and sufficiently interpret microseismic measurements. Indeed determining parameters such as fracture conductivity may well be beyond the ability of a single technology to image, and may only be possible through data integration and model calibration. The limitations can be reduced by integrating microseismic interpretations and complex fracture modeling with geological and geophysical data, log measurements, reservoir geomechanics, and production modeling. However, the integration of such diverse and often complex datasets can be an overwhelming task and thus has not been a routine part of microseismic applications. With the recent focus on common integration platforms and data sharing between various software packages, engineers and geoscientists will soon be able to efficiently leverage numerous data and measurements to better interpret and apply microseismic measurements.

Microseismic to Simulation Workflow

This paper details a workflow that integrates MSM with G&G data, newly-developed complex fracture models, geomechanics, and reservoir simulation to improve completion strategies and stimulation designs in unconventional reservoirs (Figure 1). This workflow is intended to emphasize the interrelationship of the various components and the need to constantly back-integrate the results from each domain and measurement. For example, hydraulic fracture mapping can be used to constrain or calibrate complex fracture propagation models. Conversely complex fracture modeling can aid in the interpretation of MSM. The results from any one component are frequently required to reduce the uncertainty in the interpretations from other components – essentially seeking a solution that is consistent between all components. Portions of this workflow have been documented by Fisher 2002, Cipolla 2009c, Du 2009, Warpinski 2009, Xu 2009, and Du 2010.

Currently, extracting the hydraulic fracture structure from microseismic measurements is a difficult task that cannot be accomplished independently. However, understanding the hydraulic fracture structure is the first critical task in improving stimulation designs. The next is understanding the location of proppant within the hydraulic fracture and the distribution of fracture conductivity. The final task is developing a reservoir model that can accurately predict well performance. The accomplishment of these tasks requires a significant amount of information to develop a reliable Earth Model with a robust description of the reservoir and rock mechanical properties, plus the location and distribution of natural fractures, faults, and other geologic features. A very detailed Earth Model incorporating geological and geophysical (G&G) information is required to optimize completion design and field development strategies in unconventional reservoirs. The workflow illustrated in Figure 1 will typically utilize sonic logs to provide both vertical and areal (logs in horizontal wells) distributions of rock properties and stress. These logs are calibrated to core and fracture treatment data. Specialized sonic logs can provide an estimation of both minimum and maximum horizontal stress, a key parameter when modeling complex fracture propagation. Borehole image logs in both vertical and horizontal wells (when available) are used to identify and characterize natural fractures and the log measurements are then extrapolated to the well and field scale using statistical realizations combined with geologic information and advanced seismic interpretations – creating a discrete fracture network or DFN. A much more reliable interpretation of the microseismic measurements is possible with a detailed description of the local structure, stress regime, and DFN.

Figure 1 - Microseismic to Simulation Workflow

Geomechanics

Hydraulic Fracture Mapping

Reservoir Simulation

Complex Hydraulic Fracture modeling

Geology & Geophysics

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Stage 1, green

Stage 2, red

Stage 3, yellow

Stage 4, blue

σh from advanced sonic log

In additional to serving as an essential input into the interpretation of microseismic measurements, the G&G of the Earth Model is also used for complex fracture modeling, geomechanical modeling, and reservoir simulation. In each case the consistency between the complex fracture, geomechanical, and reservoir models and the Earth model are evaluated and appropriate changes are made to the various models.

Introduction to Example Case History

A horizontal Barnett shale completion case history is used to illustrate this single-well-completion-focused reservoir simulation workflow and the integration of MSM, complex fracture modeling and geomechanics with G&G data. It builds upon the work of Daniels et al. (2007) and Rich and Ammerman (2010), and has a rich dataset, including MSM for all stages of the stimulation, advanced sonic logs that provided estimates of minimum and maximum horizontal stress (Daniels et al., 2007, Bratton et al., 2004, Franco et al., 2005), and 3D seismic interpretations of curvature and natural fracture orientations (Rich and Ammerman 2010). The

3200-ft lateral was drilled in the direction of minimum horizontal stress (σh), encouraging transverse hydraulic fractures, and four slickwater fracture treatments were pumped. The treatment details are summarized in Table 1. The fracture treatments were monitored using an array of geophones in an offsetting wellbore. The microseismic mapping results are shown in Figure 2, illustrating the significant variation in microseismic behavior exhibited by stages 1 and 2 compared to stages 3 and 4. Stages 1 and 2 are much

more planar fractures than stage 3 and 4, which exhibit very complex microseismic event patterns. The minimum horizontal stress (σh) from an advanced sonic log is also shown in Figure 2. The stress varies considerably along

the lateral (red is lower stress, blue is higher stress). Although the stress varies significantly even within the stage intervals, it is generally higher in the toe of the lateral, which is consistent with the treatment data instantaneous shut-in pressure (ISIP) for stage 1. The advanced sonic log also provides an estimate of maximum horizontal stress (σH), which shows significant variations along the lateral.

In addition to the advanced sonic log interpretations of minimum (σh) and maximum (σH) horizontal stress, 3D seismic data provided important insights into local variations in Barnett structure. Figure 3 shows the 3D seismic interpretation by Rich and Ammerman 2010), illustrating the significant difference in seismic attributes between the toe and heel of the lateral. Their interpretation suggests that natural fracture trends in the toe section of the lateral are parallel to the direction of hydraulic fracture propagation, while natural fractures are oriented perpendicular to the hydraulic fracture in the heel portion of the lateral.

The combination of the 3D seismic interpretation, advanced sonic log, fracture treatment pressures, and microseismic measurements provide a much more reliable understanding of differences in hydraulic fracture growth along the lateral. These data will be used throughout the paper to illustrate the microseismic to simulation workflow, integrating these measurements with advanced microseismic processing and interpretations, complex fracture modeling, and geomechanics

to provide a more reliable evaluation of well performance (i.e., reservoir simulation history match).

Figure 2 - Microseismic Event for Case History

Stage

Fluid Vol.

(bbls

Prop. Vol.

(klbs)

PadBH ISIP (psi)

Post-Frac BH ISIP (psi)

Change in ISIP (psi)

σH

gradient from Sonic Log

(psi/ft)

σh

gradient from

Pad ISIP (psi/ft)

σH – σh(psi/ft)

1 24,600 430 2161 1970 -191 0.77 0.70 0.07

2 25,400 439 1659 1908 249 0.74 0.64 0.10

3 24,700 441 1698 2262 564 0.69 0.65 0.04

4 24,800 440 1510 2619 1109 0.65 0.62 0.03

Table 1 – Case History Fracture Treatment and Stress Data

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Geophysics - Integrating Microseismic and Seismic Measurements The application of geophysics in the interpretation and application of microseismic measurements is twofold:

1. Processing of the microseismic waveforms, both to locate the events in space and time and to characterize the nature of the associated rock failure. 2. Integration of larger scale seismic measurements to aid in the interpretation and application of the microseismic measurements.

The role of geophysics in hydraulic fracturing is to image

the fractures and image and characterize the reservoir to provide information about the reservoir conditions that can control the nature of the hydraulic fracture response. Microseismicity is the primary technique to image the geometry and growth of the hydraulic fractures, although active seismic source techniques (e.g., 4D seismic as described by Atkinson et al., 2010; time lapse 3D VSP as described by Willis et al., 2009; and time lapse cross-well as described by Langan et al., 2000) could potentially also be used to image the fracture network. While these active source methods offer potential advantages to image both the hydraulically created fracture geometry as well as the final, propped fractures, they have yet to be demonstrated definitively. Nevertheless, static active seismic images have proven to be valuable in defining the geologic environment and reservoir heterogeneity that could influence the

stimulation. Examples of using seismic methods to image various aspects of the geologic environment include: directly imaging faults (e.g. Maxwell et al., 2008 or Norton et al., 2010) and assessing azimuthal anisotropy associated with pre-existing fractures (e.g. Wolfe et al., 2007, Verdon et al., 2010); imaging reservoir heterogeneity (e.g. Close et al., 2010); imaging geohazards (e.g. Roth and Thompson, 2009) and imaging structural folding (e.g. Rich and Ammerman, 2010). These studies also describe comparisons to understand the relationship between the seismic data and the hydraulic fracture as imaged by microseismicity.

The primary information about the hydraulic fracture comes from the microseismic locations through the temporal-spatial growth of the microseismic cloud. Originally, microseismic monitoring data was acquired using seismic sensors deployed in vertical offset wells. However, with field developments drilling mostly horizontal wells, the availability of vertical wells is becoming more limited and there is a trend toward deploying arrays within the deviated build section and horizontal sections. Furthermore, the use of surface or shallow well microseismic monitoring without reservoir level observation wells is also growing. While definitive integration of surface and downhole data has yet to be made, investigations have focused on comparing the relatively few, large microseismic events where signals can be discretely detected on surface (Eisner et al., 2010). The resolution limits of surface monitoring have yet to be demonstrated and appear to be restricted compared to monitoring downhole with sensors close to the target (Chambers, 2010). With the expansion of these different monitoring scenarios, it is important to understand the location accuracy differences between the various monitoring geometries, especially for the accuracy of the depth of the events which tend to vary widely. Generally, vertical monitoring wells provide the best location accuracy, followed by horizontal monitoring wells (Maxwell and Le Calvez, 2010) and then surface monitoring (Eisner et al., 2009 and Chambers, 2010). Clearly, optimizing the resolution and accuracy of locating the microseismicity is important for confident interpretation, although the acquisition configuration details are not relevant to the workflows described in this paper.

While the microseismic deformation can contain dilational modes of deformation, shear deformation still appears to be the dominant mode (e.g. Sileny, 2009). Microseismic shear failure is generally assumed to be indirectly associated with the geomechanical deformation associated with the tensile expansion of the hydraulic fractures. Shear deformation could be the result of various mechanisms, including: induced shear stresses near the tip of the tensile hydraulic fracture; ‘doglegs’ or bends in the fracture; or fluid leak-off lubricating pre-existing discontinuities. Depending on the site, the dominant frequency of microseismic deformation is generally in the range of several 10’s to 100’s of Hz, with a moment magnitude typically in the range of -4 to -2 but occasionally as large as magnitude 0 (Maxwell et al., 2008). These seismic source characteristics typically correspond to a shear

Figure 3 - Advanced seismic interpretation (from SPE 131779)

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deformation over an area of a few square meters and shear displacements up to a few millimeters. The moment magnitude is also an important quality control parameter to assess if the extent of the entire fracture network has been detected (Maxwell et al., 2006). Cumulatively the density of the co-seismic displacements associated with the microseismicity is much smaller than the typical dilation of the tensile hydraulic fracture. Similarly the cumulative seismic energy is a small portion of the total energy (1 part in a million to a billion, e.g., Maxwell et al., 2008) suggesting significant aseismic deformation (i.e. slow processes below the frequency band of the seismic sensors with relatively small seismic deformations that are not resolved below the background noise level). Nevertheless, the seismic deformation measured by the density and cumulative strength of the microseismicity can be used as a proxy for fracture density (Maxwell et al., 2006). Microseismicity should be viewed as an expression of part of the geomechanical deformation associated with the stimulation, and should be interpreted in the context of a geomechanical model.

In addition to the fracture geometry defined by the event locations, the seismic radiation pattern can also be used to estimate the deformation mode or mechanism of failure. If the seismic radiation is measured from different locations, moment tensor inversion can be used to estimate the seismic strains in all directions (e.g., Sileny, 2009). However, microseismic observations are often made in specific directions such as from a single downhole observation well, in which case analysis is limited to fault-plane orientations and slip directions assuming pure shear. Fault planes can be computed for single events or signals from multiple events can be used together to define a composite solution to jointly fit all the individual events (Rutledge et al., 2004).

Figure 4 shows the location of microseismic events for two fracture treatments in the Cotton Valley sands, with very planar event patterns (uppermost graph) indicating relatively simple fracture growth (Rutledge et al., 2004). Figure 5 shows the composite fault plane orientation calculated from the microseismic data, indicating shear slippages in approximately the same direction as the fracture propagation. In this example the dominant natural fracture trend is similar to the hydraulic fracture direction, which likely contributes to the relatively planar fracture growth. The ability to extract information to characterize the natural fractures from the microseismic data can aid in evaluating stimulation designs and well performance.

As described above, the predominantly shear microseismicity represents only part of the geomechanical response, and so using estimated fault planes may not directly correspond to the hydraulic fracture plane. Indeed a tensile fracture opening in the direction of the minimum principal stress would by definition have no resolved shear stress or

strain. However, fracture planes at even a small angle to the hydraulic fracture would tend to shear, especially near the fracture tip. Composite fault plane solutions from multiple events have the apparent benefit of averaging the variability in fracture and stress rotations, and ideally would average to the hydraulic fracture geometry. If available, specific fault plane solutions and moment tensor inversion results can therefore be used along with the microseismic locations to define a DFN from the microseismic cloud.

The primary details of a DFN can be defined by interpreting microseismic locations. While the microseismicity may be located away from the hydraulic fracture, the majority of the microseismic events would tend to be consistent with the location of the hydraulic fracture within location uncertainties (Warpinski et al., 2004). In some specific cases, induced stresses can potentially cause activation of pre-existing fractures or faults at some distance from the hydraulic fracture (e.g., Maxwell et al., 2009) but it is not believed to be a pervasive mechanism. To accurately define the proper location and extent of a DFN, it is critical to ensure that the events are accurately located, because apparent complexity could be a result of mislocated events associated with large uncertainty. In the case of the main case study of this paper, the change from simple to complex fracture patterns is significant relative to location uncertainty.

Figure 4 - Microseismic locations from Cotton Valley project (AfterRutledge et al. 2004). Note the relatively long, narrow region ofmicroseismicity for each well indicating relatively simple fracturegeometry.

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Geophysics - Example Case History Using the central case study in this paper as an example, a quality control and interpretation workflow can be defined for the

microseismic results:

Confirm the location and accuracy of the microseismic events. Process the seismic signals recorded from perforation shots to estimate the shot locations using the same processing used for the microseismic event locations. Validate the processing by checking the location accuracy, and consistency of observed and modeled arrival times and directions (e.g., Maxwell, 2009).

Confirm the interpreted hydraulic fracture geometry. Compare the extent of microseismic clouds with estimated location uncertainties. Check if the extent of the microseismic clouds is within the location uncertainty, or whether the cloud dimensions can only be explained by fracture geometry. For example if the width of the microseismic cloud is consistent with the average location uncertainty, the apparent complexity is most probably related to microseismic statistical location errors rather than true fracture complexity (e.g., Maxwell, 2009).

Confirm that all the hydraulic fracture geometry was detected. Check plots of magnitude versus source-sensor distance to verify that a range of magnitudes is recorded at the greatest offset. If the minimum detected magnitude at the greatest offsets is comparable to the magnitude of the largest microseismicity then microseismicity at larger offsets may not have been detected and the fracture geometry may be larger than estimated.

Confirm interpreted hydraulic fracture orientations. Check composite plots of observed amplitude ratios with inferred fault plane solutions to see if single or multiple fracture planes are required to explain the fault plane solutions.

The following conclusions can be drawn from the microseismic image shown in Figure 2.

- The location accuracy of the microseismic events is within 10’s of feet.

- The NE-SW orientation, fracture lengths and heights are not affected by location uncertainty.

- Stages 1 and 2 are consistent with relatively few, parallel fractures.

- Detectability indicated that microseismicity could be recorded at greater offsets and that the entire geometry is believed to have been recorded.

- Composite fault plane solutions confirm simple strike-slip shear failure for stage 1 and 2, while stage 3 is consistent with strike-slip shear failure on planes in multiple directions.

Furthermore, the apparent simple fracture geometry for stages 1 and 2 and the complex geometry of stages 3 and 4 are consistent with 3D seismic interpretation. Figure 3 shows an overlay of the microseismicity with reservoir characterization estimates of the expected fracture response based on seismic curvature and anisotropy (Rich and Ammerman, 2010).

Figure 5 - Top plots show composite focal mechanisms for hydraulic fractures inthe Cotton Valley sands after Rutledge et al., 2004. Top shows fault planesolutions for two different fracturing fluids and corresponding groups of events.Bottom is a plot of horizontal shear amplitude to compressional wave amplitudesversus azimuth of the microseismic event to the sensors. The solid linesrepresent theoretical curves for vertical strike slip shear faulting.

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Geomechanics for Microseismic Interpretations and Applications The role of geomechanics in the interpretation and application of microseismic mapping measurements is evolving rapidly, but geomechanics has yet to be routinely utilized in microseismic workflows. The reasons for this under-utilization are twofold: a lack of geomechanical models focused on the interpretation and application of microseismic data and the inability to easily integrate geomechanical models into microseismic workflows. However, the continued growth of microseismic mapping, especially in shale-gas development, has highlighted the need for integrating geomechanical models with microseismic applications. Warpinski 2009, Warpinski et al. 2004, Palmer et al. 2007, Settari et al. 2002, and Zhao and Young 2009 have all documented the potential application of geomechanics to improve microseismic interpretation.

Geomechanics has two main roles in microseismic mapping: 1. Geomechanical analyses are used to provide a better definition of the environment in which the hydraulic fracture

network propagates. The three primary sets of parameters involved are the geometry and structure (including layering, faults and fractures), material properties (elastic and failure parameters), and the in-situ conditions (stress, pressure and temperature). All of these, especially the first and last, may be changed by the fracturing treatment.

2. Geomechanics is used to enhance the interpretation, by using well-established principles of mechanics to provide a physical explanation of the observed microseismic events.

Definition of the environment in which the fracture propagates Models of complex fracture networks, such as those discussed later in this paper, require a more complete description of the

stress field than prior simple models of vertical planar fractures. In addition to the minimum horizontal stress, the maximum horizontal stress and indeed the full 3D stress field play an important role in modeling fracture propagation using these complex fracture models. Wellbore measurements (e.g., advanced sonic logs) can provide estimates of both minimum and maximum horizontal stresses near the wellbore in addition to mechanical properties such as Young’s modulus and Poisson’s ratio. The material properties can be extrapolated across a wider area using techniques such as kriging and neural networks. However, the stress field should be calculated with a 3D geomechanical simulator, using the wellbore measurements as calibration points, to ensure that both the properties and the structure are accounted for correctly in the stress field.

The other primary factors controlling hydraulic fracture complexity are the distribution and properties of natural fractures (NFs). Discrete Fracture Network (DFN) models are used to simulate production in naturally fractured reservoirs. They were developed to overcome the limitations of Dual Porosity models, accounting for anisotropy, heterogeneity and scale-dependent connectivity (Dershowitz et al. 1998). Qui et al. (2001) correlated fracture density with curvature, and Sayers et al. (2001) used azimuthal variation to characterize fracture orientation and density. Will et al. (2005) integrated seismic wave information to better describe fracture orientation and density. Both these and log-based approaches (e.g. Bratton et al. 2007) are primarily descriptive. Their purpose is to characterize the structure of the NF network, by using seismic information to extend observations at the wellbore across the reservoir.

If the natural fractures are oriented in the preferred hydraulic fracture direction it is difficult for fluid to travel in any other direction except through leakoff, unless the rock matrix fails. If the natural fractures are oriented at a high angle to the hydraulic fracture direction, fluid movement will depend on the fracture density and conductivity. The initial conductivity is dependent on the diagenesis of the reservoir during and after their creation. However, during fracturing, the conductivity depends on how they react to changes induced by the hydraulic fracturing process, and post-fracture conductivity is dependent on the amount of dilation remaining after the fracture network has been de-pressurized. The application of complex fracture models thus requires more advanced geomechanical analysis. In addition to the NF geometry, the mechanical and flow properties (stiffness, strength and permeability) of the NFs must be characterized to enable prediction of network evolution during a treatment.

Classical geological restoration (Dahlstrom 1969) is a process in which geometric principles are used to simulate the phenomena resulting in the formation of geological structures. More recently (Macé et al. 2005), geomechanical principles have been added to the process. The processes involved in forming the current geologic structure are simulated, to develop a 3D stress field consistent with the resulting deformations. This 3D paleostress-field is used both to constrain the interpretation of the microseismic map, and to predict the orientation and likelihood of natural fractures. Hussein et al. (2010) have defined a process for using a geomechanical simulator to model reservoir failure during production. This process can also be used to simulate the geological evolution of the reservoir, resulting in a model of the current stress state and NF network for use in HF simulation and interpretation of MS mapping.

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Physical Explanation of Microseismic Events There are three possible sources of microseismic events during hydraulic fracturing, namely opening at the fracture tip, failure

of intact rock in regions other than the fracture tip, and failure on pre-existing natural features. It is generally assumed that most of the observed microseismic events are shear failures, either of intact rock, or more likely on existing planes of weakness, such as faults or natural fractures (Maxwell et al. 2008).

In hydraulic fracturing, significant volumes of proppant are placed, and large volumes of fluid are pumped at high rates into low-permeability formations (in this case study, approximately 140,000 ft3 (4,000 m3)). Clearly, this requires large-scale tensile opening. Maxwell et al. (2008) have calculated that only a tiny fraction (on the order of one billionth) of the total energy associated with fluid injection is detected in microseismic events. The rest is consumed by frictional losses and the relatively slow opening of tensile fractures, which would not be detected by microseismic monitoring. Induced fractures propagate based on the local stress directions, and thus may turn towards faults or other features, depending on the local stresses, as described by Elbel and Mack (1993) for re-fracture treatments. Although the primary deformation is tensile, micro-cracking ahead of the crack tip may be detected as microseismic events. Warpinski (2009) has noted that this shear zone is usually narrow. Detected events would thus provide a good indication of fracture location if they could be definitively associated with the tip of a hydraulic fracture.

Shear failure of both intact rock and natural fractures is usually represented by frictional models such as the Mohr-Coulomb criterion. Whether failure occurs mostly in intact rock or along pre-existing natural features depends on their relative strength, represented by the parameters of the failure criteria. There is clear evidence of a wide range of strengths and porosities in natural fractures (Gale et al. 2007), but generally, natural features are weaker than intact rock, even if they have “healed”.

Natural fractures may slip due to changes in the far-field stress induced by deformation during hydraulic fracturing. Theoretical solutions for the stress in homogeneous elastic materials (e.g. Sneddon 1946) can be used to show that a large planar fracture will impact the stress in a region approximately one height away from the fracture. This stress-shadowing effect, in which the stress from one fracture (Warpinski and Branagan 1988) influences the growth of others, is estimated at a few hundred feet (Fisher et al. 2004) in the Barnett shale. In many cases, stress changes alone are insufficient to induce slip on NFs unless they are lubricated. Failure is controlled by effective stresses (σtotal – p), and in addition, the friction coefficient of a lubricated surface is usually lower than that of a dry surface. Hence, fluid penetration into a natural fracture can trigger failure. Olsen et al. (2007) described some of the geomechanical criteria for opening NFs along the face of a propagating HF. These can be generalized to apply to all NFs, by including not only full opening, but also the increased conductivity caused by dilation.

Geomechanical simulation can indicate whether NFs are fluid-activated or stress-activated during a hydraulic fracture treatment. If they are fluid-activated, it can be inferred that injected fluid has penetrated them, and they are thus connected to the wellbore. However, fractures that are activated by the stress change alone may not be connected to the wellbore.

As noted above, NF failure (slip) may occur if the effective stresses change. In addition to deformation caused by the opening of the large-scale hydraulic fracture, some of the other causes of effective stress changes in intact rock are:

• Pore pressure changes due to fluid leakoff into the formation. • Poroelastic effects, in which the fluid pressure in the formation changes due to pore volume changes. • Thermoelastic effects, due to cooling of the rock around the wellbore and fracture.

The timescale of typical hydraulic fracturing treatments is too short to result in appreciable thermoelastic effects, and poroelastic effects are usually minimal in low-permeability gas reservoirs because the gas is highly compressible and fluid leakoff into the formation is minimal. Thus, failure of intact rock (and the associated microseismic events) will be caused by deformation associated with stress-shadowing. Permeability Changes and the Impact on Production

The permeability of natural fractures is affected by their opening, e.g. due to reduced normal stress (Walsh 1981). Chipperfield et al. (2007) showed that this could result in a tenfold increase in permeability during a typical HF injection. When a natural fracture slips, it dilates due to the natural roughness of the fracture (Barton et al. 1985). This dilation of the feature is an additional form of opening, and further increases its permeability. Chipperfield at al. showed that this could result in a thousand fold increase in permeability. This large permeability increase may result in further fluid penetration, which can both increase the pressure and extend the penetrated zone, resulting in further failure.

If the dilation and slip are purely elastic, they are reversed when the fracture closes, e.g. due to leakoff or flowback. Unlike dilation induced by normal stress changes however, shear failure is typically not reversible. The NFs therefore retain their enhanced permeability after the HF treatment ends.

One of the key parameters in the design of conventional hydraulic fractures is the fracture conductivity. The fracture is only successful if it provides a high-conductivity pathway to the wellbore (relative to reservoir permeability). Any permanent dilation of natural fractures may result in a high relative conductivity, especially in a very low-permeability rock. Thus un-propped NFs can contribute to production in the same manner as propped HFs.

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Geomechanical Interpretation of the Case Study Geomechanics concepts have been applied to confirm other measurements and interpretations, including the curvature, natural

fracture characterization and wellbore stress field. As noted previously, there are three sets of parameters which control the interpretation of MS activity, namely the geometry, the material properties and the in-situ conditions.

Olsen et al. (2007) have identified the importance of tectonic events in creating natural fractures. In this case, the fractures may be fold-related. A highly-simplified geological restoration of the reservoir was performed, using a geomechanical simulator. General features of the geological history were simulated, to develop a representation of the modern-day stress field, and to understand the mechanics of the origin of the natural fractures. Figure 6 shows the simulated stress anisotropy in a small section of the model, which is consistent with the curvature analysis in Figure 3. The stress can be used in a more complex fracture simulator which accounts for the 3D stress field.

Hydraulic fracture complexity is controlled by the interactions between natural fractures and hydraulic fractures. The stress field and the distribution and properties of the NFs impact these interactions. It is intuitively obvious that high stress anisotropy should limit the re-opening of fractures perpendicular to the maximum horizontal stress, in addition to preventing the initiation of new fractures. Chuprakov et al. (2010) have quantified these effects numerically. The variation in σh and σH along the lateral (obtained from advanced sonic logs) were noted in the introduction to the case study. This variation is also supported by stress estimations inferred from an integration of advanced sonic logging and ISIP observations (Daniels et al. 2009). The horizontal stress anisotropy (σH - σh) is lower near the heel than the toe.

Rich and Ammerman (2010) have shown that the natural fractures in the area of Stages 3 and 4 are most likely oriented parallel to the minimum stress direction, in contrast to the toe of the well where they are aligned with the preferred hydraulic fracture plane. The stress anisotropy and NF orientation thus both favor NF opening in the last two stages of the treatment.

In stages 1 and 2 (Figure 2) the microseismic cloud is roughly consistent with a relatively planar fracture initiated at the toe-most perforation cluster, and a smaller one closer to the heel. The natural fractures in this part of the formation are closely-aligned with the preferred hydraulic fracture plane. Under these conditions, even if they shear or open under pressure, the predominant direction of propagation will be unchanged. Furthermore, the difference in stress gradient of 0.07-0.10 psi/ft (Table 1) corresponds to a stress difference of several hundred psi, which is sufficient to prevent initiation of new fractures perpendicular to the original fracture.

Stages 3 and 4 both show a much more diffuse cloud of MS events than stages 1 and 2 (Figure 2). There is evidence of planar features in stage 3 in the region that transitions from high to low stress anisotropy. Significantly, this part of Stage 3 lies in the area in which the NFs are aligned with the preferred HF plane. A large proportion of the activity in Stage 3 forms a cloud in the area around the uphole perforation clusters. The natural fractures in this area are aligned roughly perpendicular to the preferred HF plane, and the stress anisotropy is relatively low (Figure 3). These two factors favor the opening of natural fractures. Stage 4 is similar to Stage 3, but planar features are practically non-existent, and the MS cloud clearly overlaps significantly with that of Stage 3.

Complex fracture modeling Microseismic monitoring clearly indicates that complex fracture networks are often developed during hydraulic fracturing treatments of shale gas formations. This complexity is a result of pre-existing natural fractures in the formation, low stress anisotropy, the use of low viscosity fluid, and possibly other mechanisms. Conventional hydraulic fracture models, developed to simulate bi-wing planar fractures, are adequate for non-fractured formations or where high stress anisotropy favors planar fracture

Figure 6 - Stress anisotropy from geomechanical simulation

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propagation. However, these planar models are inadequate for simulating complex fracture geometry in shale gas. The recent development of complex hydraulic fracture propagation models (Xu et. a. 2009a, 2009b, 2010 and Meyer 2009) provides key elements to constrain the interpretation and enhance the application of microseismic measurements that have been lacking in the past: mass balance and fracture mechanics.

Historically, there has been a large amount of research on fluid flow in naturally fractured formation, including both injection and production. These works are associated with applications such as simulation of production from fractured formations, injection into highly fractured geothermal formations, and fluid flow in fractured rock surrounding underground structures, and are not reviewed here due to space limitations. The models developed in these applications generally assume a pre-existing, highly-connected, natural fracture system. In fact, only the connected fracture network contributes to flow. The applicability of these models to hydraulic fracturing is limited because hydraulic fracturing creates fracture paths where natural fractures may either not exist or not be connected initially. Therefore, a complex hydraulic fracture model must simulate the hydraulically created fracture network, rather than just the existing natural fracture network. Additionally, many of the numerical models capable of simulating discrete fractures are computation-intensive, which makes them poorly-suited to day-to-day use for hydraulic fracture treatment design and analysis. Olson (2008) extended a numerical model developed previously for prediction of natural fracture creation under tectonic deformation over geologic time for prediction of fracture network creation during hydraulic fracturing treatment of a horizontal well. While the model provides insight into fracture interaction, it has limited applicability since it assumes a constant fluid net pressure. Fluid flow in the fracture network and its interaction with the fracture width are not taken into consideration.

Semi-Analytical Model

To properly simulate the complex fracture network developed during hydraulic fracturing, the fracture model must simulate fluid flow in the created fracture network, and account for the interaction between the fluid and rock deformation (i.e. fracture width), as well as interaction between the fractures. Xu et al. (2009a and 2009b) presented a model, referred to as the Wire-mesh model, which simulates fracture network propagation during a fracture treatment. The model has been extended to include fracture height growth, proppant transport simulation, and propagation from multiple injection points in Xu et al. (2010). In this model, the hydraulic fracture network (HFN) is represented by an elliptical region consisting of two sets of parallel, uniformly spaced, vertical fractures along the horizontal principal stress directions (Figure 7).

The shape and growth of the HFN depend on many factors such as rate of fluid injection, viscosity of the injected fluid, fracture spacing (dx, and dy), formation mechanical properties and the stress field. The Wire-mesh model solves a set of equations governing fluid flow in the Wire-mesh fracture network, elastic deformation of the fracture (width-pressure relationship), fracture height growth based on the conventional pseudo-3D model (Mack and Warpinski 2000), and overall mass balance. Mechanical interactions between the fractures are also included in the model.

Similar to a conventional fracture model, the input parameters include vertically-layered zone properties and minimum stress profile, completion and perforations, fluid and proppant properties, and pumping schedule. Additionally, the model requires stress anisotropy (difference between maximum and minimum horizontal stresses, denoted Δσ), and fracture spacings dx and dy in the primary and secondary fracture directions as input. The model then predicts the evolution of fracture dimensions (height h, major axis a, and minor axis b), treating pressure, fracture width and proppant placement in the HFN as functions of time. The model degenerates to a bi-wing fracture if the net pressure in the fracture is insufficient to open up the secondary fractures perpendicular to the maximum horizontal stress.

The fracture network parameters dx and dy can be estimated from sources such as image logs, 3D seismic interpretations, core and outcrop studies to constrain the modeling. The stress anisotropy Δσ may be estimated from advanced sonic logs, regional geological studies, and geomechanical evaluations of borehole breakouts. If these parameters are not known, they become free model parameters, and their values will affect the predicted fracture dimensions. To obtain a more definitive result, microseismic measurements can be used to constraint the model by providing a measurement of the overall network dimensions. Unknown or uncertain parameters (e.g. dx and dy) can be adjusted to “calibrate” the complex fracture model. The application of the Wire-mesh model will be illustrated using the example case history.

Numerical Model

While the Wire-mesh model provides an estimate of the fracture network dimensions and proppant placement in the network, it has some limitations. One of the limitations is that the fracture network pattern (i.e. the frac spacings) cannot be directly linked to the pre-existing natural fractures. Calibration against microseismic data is required to obtain fracture spacings consistent with the stimulated volume indicated by the microseismic data. However, the calibrated fracture spacings cannot easily be used in other wells, or even other stages, if the reservoir characteristics and pumping parameters change significantly. When used for parametric studies to optimize a treatment, the outcome may be skewed because the model assumes fixed spacings and any effect of treating parameters on the resulting fracture network pattern is not taken into account. Another limitation is that network

SPE 133877 11

geometry is assumed symmetric with respect to the injection point and the shape is elliptical, limiting the ability to match microseismic data that shows significant asymmetry or irregular shape of the stimulated region.

To properly take into account the influence of pre-existing natural fractures and to predict the complex fracture network geometry without presumption of its pattern, a more rigorous fracture simulator for complex HFN has been developed, referred to as the Unconventional Fracture Model (UFM). The model is based on many assumptions similar to conventional pseudo-3D fracture models, but instead of a single planar fracture, it simulates the propagation of interconnected fracture branches in a complex fracture network. The model solves the fully coupled problem of fluid flow in the complex fracture network and the elastic deformation of the fracture width. Fracture height growth is modeled in the same manner as in conventional P3D models. A proper fracture tip propagation criterion is adopted and overall mass balance is satisfied. Currently, 1D fluid flow and proppant transport in the fracture network is implemented. A three-layer model is adopted for simulating proppant transport, consisting of a proppant bank at the bottom, a slurry layer in the middle and clean fluid at the top, for each element of the fracture network. Transport equations are solved for each component of the fluids and proppants pumped.

A key component of the UFM is the ability to simulate the interaction of a hydraulic fracture tip with a pre-existing natural fracture when they intersect, i.e., whether the hydraulic fracture propagates through, or is arrested by, the natural fracture, which may open and propagate. The branching of the hydraulic fracture at intersections with the natural fractures gives rise to the development of a non-planar, complex fracture pattern. An extension of the Renshaw and Pollard interface crossing criterion, applicable to any intersection angle, has been developed (Gu and Weng, 2010). In addition to the hydraulic fracture-natural fracture interactions, the UFM also takes into account the interaction among adjacent hydraulic fracture branches by computing the “stress shadow” effect on each fracture element by the adjacent fracture.

A complete description of the UFM model is not possible in this paper due to space limitations and will be the subject of a separate paper. Figure 8 shows an example UFM simulation for a complex fracture network developed in a formation with pre-existing fractures (shown as blank rectangles in the figure). In this example, a complex fracture network develops as a result of very low stress anisotropy. Increased stress anisotropy will result in a dominant planar fracture with some side branches or a single bi-wing fracture if net pressure in the fracture cannot overcome the stress anisotropy. The interfacial friction coefficient can also affect the fracture pattern. The combination of low friction and low stress anisotropy results in hydraulic fracture branches being arrested by the natural fractures, creating fracture complexity.

The UFM provides a tool to more accurately predict the fracture network geometry and proppant placement than the Wire-mesh model, if the pre-existing natural fractures are well-characterized. It can also be used to investigate the effect of existing natural fractures on the expected fracture geometry and their potential impact on production, and to investigate the impact of various treating parameters to help optimize the completion and treatment design. A complex fracture model like UFM provides

Figure 7 - Ellipsoidal representation of a stimulated reservoir formation of 3D schematic view (a) and map view (b). The elliptic fracturenetwork in the map view has major axis a and minor axis b. They are along the direction of maximum horizontal principal stress σH andminimum horizontal principal stress σh, respectively. The average spacing of the two fracture sets are dx and dy, respectively.

h2a

2b

(a) (b)

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more freedom to allow engineers to better match the observed microseismic measurements. The model accounts for non-uniform stress distribution and mechanical properties in the formation and can hence simulate asymmetric geometry and irregular fracture patterns. On the other hand, a complex model demands more and better-defined input parameters, and presents greater challenges to the engineer to use properly.

The UFM provides an important link between rock heterogeneity, rock fabric (i.e., natural fractures), and the hydraulic fracture geometry. Its integration in the stimulation workflow requires consideration and proper characterization of the natural fractures in the reservoir being fractured. It provides a critical tool to integrate reservoir characterization, stimulation simulation, and production for shale gas stimulation optimization.

Case History – Complex Fracture Modeling using Semi-analytical Model

To illustrate how complex fracture modeling can be used to estimate hydraulic fracture dimensions and proppant placement in the fracture network, the fracture treatments in the example well described earlier were simulated using the Wire-mesh model. The fracture modeling results presented in this section should be considered preliminary, because the application of complex fracture models is new and the models are evolving rapidly. Thus, the results from these models are still being evaluated and procedures for their application being developed. However, they provide an essential component of the microseismic to reservoir simulation work-flow that has been absent in the past, namely the ability to constrain the fracture spacing and estimate the location of proppant within a complex fracture network.

Since microseismic data indicate that most of the microseismic events are confined to depths between 6900 ft and 7400 ft, a constant

height of 500 ft is assumed in the fracture model. The stress data reported by Daniels et al. (2007) and the treatment volumes shown in Table 1 were used for simulation of the hydraulic fracture network created in each stage at different locations along the horizontal well, with an injection rate of 80 bpm for all stages. A Young’s modulus of 4 million psi and a Poisson’s ratio of 0.24 were used for all simulations. The fracture spacing and fluid efficiency were adjusted to match the microseismic event patterns for each stage. The results of the fracture modeling are provided in Table 2. The table shows the fluid efficiency, propped lengths and stress anisotropy for each fracture modeling case along with other results from the modeling. The fracture spacing (dx and dy) and estimated stimulated volume (ESV) are provided for complex networks modeled using the Wire-mesh model (denoted wm in the table).

Table 2 - Complex Fracture Modeling Results: Example Case History

Stage

Fluid Eff. (%)

σH – σh

(psi)Dx(ft)

Dy(ft)

ESV (ft3)

Surface area (ft2)

xfx

prop (ft)

xfy

prop (ft)

1 7 480 NA NA NA 2.2e6600600

NA

2 7 690 NA NA NA 2.6e6500600600

NA

3 -wm 40 100 50 50 6.8e8 3.3e7 150 100

3 -frac 40 300 NA NA NA 1.2e6 400 NA

4-wm 40 100 50 100 8.8e8 4.1e7600 200

100 30

Figure 8 - simulation of UFM model (blank rectangles represent pre-existing natural fractures)

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As discussed, the microseismic event patterns for stages 1 and 2 indicated relatively planar hydraulic fractures, consistent with a high stress anisotropy (Daniels et al., 2007) and a dominant natural fracture direction coincident with the direction of fracture propagation (Rich and Ammerman, 2010, Figure 3). The fracture modeling results for these stages showed planar fractures. A fluid efficiency of 7% for stages 1 and 2 was required to match the measured fracture dimensions. This low fluid efficiency was attributed to pressure-dependent leakoff (opening of secondary fractures) and/or complex fracture propagation due to interaction with natural fractures. Two separate fractures were modeled in stage 1 and three separate fractures were modeled in stage 2. Table 2 lists the results for each fracture. Although fracture growth is clearly planar for stages 1 and 2, the resolution of the microseismic data is insufficient to evaluate small scale complexities in fracture growth and these complexities are approximated by reducing the fluid efficiency. The fracture modeling results for stage 2 are compared to the microseismic data in Figure 9, showing a dominant planar fracture and secondary planar fractures on either side of the dominant fracture. The dominant fracture was interpreted based on the density and dimensions of microseismic events. Although the created fracture lengths (2xf) range from 1100 ft to 2800 ft, the propped lengths are only 500-600 ft. Proppant concentrations ranged from 0.25-0.75 lb/ft2 with

propped fracture heights averaging about 250 ft. The stage 3 fracture treatment straddled the two stress/natural fracture regimes (Figure 3), with minor planar fracture growth

indicated to the northeast and a large complex fracture network throughout most of the targeted area (Figure 2). Thus, Table 2 includes both planar (denoted “frac”) and network (denoted “wm”) results for stage 3. The modeling indicated spacing for the network fractures was 50ft x 50ft.

The fracture networks predicted using the Wire-mesh model for stage 4 are shown in Figure 10, illustrating the two separate fracture networks that were modeled for this stage. The modeling indicated a network fracture spacing of 50 ft parallel to the wellbore trajectory (dy), consistent with the stage 3 results, and a fracture spacing of 100 ft transverse to the wellbore (dx). Increased local stresses from the stage 3 fracture treatment could have contributed to the larger dx fracture spacing in stage 4, as the stage 4 fracture treatment overlapped the stage 3 treatment (Figure 2). The stage 4 modeling indicated a network length of about 2400 ft and network width of 1200 ft, with an ESV of 880 million ft3 and a total fracture surface area of 41 million ft2. The complex fracture network created during the stage 4 treatment resulted in over 15 times more fracture surface area than the stage 2 and 3 planar fractures and contacted a much large volume of reservoir rock. However, only a small portion (about 10%) of the fracture network is propped, shown by the region outlined by dashes in the vicinity of the wellbore (Figure 10). Therefore, much of the fracture network is un-propped and will have very low conductivity, probably around 0.1 to 2 md-ft based on the work of Fredd et al. (2002). In addition, the propped region of the fracture network may contain low concentrations of proppant depending on the complexity of the network. The 440 klbs of proppant pumped in stage 4 is distributed over approximately 15,800 ft of total fracture length, resulting in an average proppant concentration of about only 0.1 lb/ft2 for a propped height of 250 ft. It is interesting to note that the propped regions from the modeling appear to coincide to some degree with the highest density of microseismic events.

The fracture modeling results provide an important constraint for subsequent reservoir simulation history matching. However, the fractures should be discretely modeled to fully utilize the complex fracture modeling results in the reservoir simulations (Cipolla et al., 2009c,d). An important output of the fracture modeling is the propped and un-propped fracture geometry. Unlike most conventional reservoirs, un-propped fractures in shale-gas reservoirs may materially contribute to production and affect drainage patterns (Cipolla et al., 2008a and 2009b). In addition, complex fracture modeling provides an estimate of proppant

Figure 9 - Stage 2 Fracture Modeling Results compared to microseismic event patterns, Example Case History

2800 ft

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concentration in the propped region of the network, which in many cases can be very low due to the extreme complexity of the network, and may impact production (Cipolla et al., 2008a and 2009f). By constraining the fracture geometry and location of proppant, the primary history matching parameters for the reservoir simulation become the propped and un-propped fracture conductivity (assuming matrix permeability can be estimated from core and log measurements).

As noted, complex fracture modeling is very new to shale-gas evaluations and other methods which have been used to integrate microseismic data into reservoir simulation workflows are discussed in the following section. Reservoir Simulation: Determination and Application of Hydraulic Fracture Structure and Stimulated Volume The application of microseismic fracture mapping measurements requires estimation of the structure of the complex hydraulic fracture or the volume of the reservoir that has been stimulated by the fracture treatment. When fracture growth is predominately planar, the interpretation of microseismic measurements reduces to determinations of hydraulic fracture length, height, azimuth, and location. However, in many shale-gas reservoirs, fracture

growth is complex and it is important to describe the nature and degree of the complexity. The geophysical processing results, geomechanical modeling, and complex fracture modeling provide the basis for extracting hydraulic fracture structure and stimulated volume when fracture growth is complex. The fracture structure or stimulated volume is the starting point for reservoir simulation history matching or production forecasting. There are two primary approaches used to incorporate microseismic measurements into reservoir simulation models: discrete modeling of the complex fracture network and a dual porosity representation of the stimulated volume. A number of methods in these two broad categories are employed to model production behavior from complex fracture networks (discussed below). The application of both discrete modeling and dual porosity approaches will be illustrated later in the paper using the example case history.

In all cases the microseismic measurements are assumed to represent the most likely location of the hydraulic fracture network or primary fracture planes and the volume of the reservoir that has been stimulated by the fracture treatment. The two most widely used techniques for determining stimulated volume are the Estimated Stimulated Volume or ESV (Daniels et al. 2007) and the Stimulated Reservoir Volume or SRV (Mayerhofer et al. 2008). These methods utilize the location, continuity, and density of microseismic events to define the stimulated volume and can be normalized using magnitude-distance relations. The ESV method will be used in this paper to calculate stimulated volume and is illustrated in Figure 11. The ESV for the four stages varies significantly, ranging from 75 million ft3 for stage 2 to 320 million ft3 for stage 3. In addition, Figure 11 shows significant overlap of the stimulated volumes from stages 3 and 4. The implications and applications of the ESV interpretations will be discussed in the later section detailing the case history reservoir simulation history match. Approximating Hydraulic Fracture Structure – Modeling Discrete Fractures

One approach to resolving fracture planes has been to consider the events in short sets. Fisher et al. (2002) used this approach with a set-size of 40 events and performed a linear regression on each set; these were then treated as fracture segments. The results from such analysis can be interpreted as indicated fracture spacing, and using this interpretation, Cipolla et al. (2009c,d,e) provide production simulations of regularly spaced orthogonal fractures within an identified Stimulated Reservoir Volume (SRV). In that work differences between primary and network conductivity are investigated for their potential impact on well performance. Mayerhofer et al. (2006) show a ‘network structure’ overlain on results of a Fisher-type analysis (Figure 1 of that paper). This network structure is not a subset of the results of the Fisher analysis, but instead is produced by iterating over a simulation model, which appears to be based on regular spacing. Details of how the network structure relates to the underlying Fisher analysis are

Figure 10 - Stage 4 Fracture Modeling Results compared to microseismic event patterns, Example Case History

2400 ft

1200 ft

Approximate location of proppant

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not provided in that paper. In a more recent paper Mayerhofer et al. (2008) provide details of a Stimulated Reservoir Volume, although it is stressed that this concept only relates to production via an estimate of fracture spacing within that volume. Cipolla et al. (2008a) discuss the effects of proppant placement in a network structure and the potential impact this has on production. The work has been summarized in a recent review by Cipolla (2009f).

Quantitative Identification of Planes – Modeling Discrete Fractures Williams et al. (2010) provide a probabilistic approach to determining the number and orientation of major fracture planes

given a set of microseismic events with uncertain locations. They define the number density of a microseismic event to be distributed across its location’s uncertainty ellipsoid and use this information, via a continuous modification of the Hough Transform (Hough, 1962), to determine the number and locations of the major planes indicated by the data. The solutions for multiple planes are considered and an a posteriori distribution of the number of planes is constructed using a method related to that proposed by Sivia and Carlile (1992) in the field of molecular spectroscopy. The result of this analysis is a set of solutions for different numbers of planes, where the relative likelihood of each solution is well known. These likelihoods can then be used in the construction of a multiple realization production prediction, which translates the uncertainty in the plane interpretation into the P10, P50 and P90 estimates of future production. Dual porosity approach

A further approach is to treat the results of fracture mapping, either from microseismic or surface seismic, as an indication of the underlying stochastic distribution of fractures (Williams-Stroud, 2008). This approach was taken by Zhang et al. (2009), who used this information to derive dual porosity reservoir models, with major transverse fractures modeled explicitly. They, too, examine the effects of spacing for transverse fractures and included the potential effects of non-Darcy flow on the production. Their work demonstrated that the results of this type of model are most sensitive to the matrix-fracture transfer function (‘sigma’) which is estimated from multiple realizations of the possible fracture distribution. Du et al. (2009) construct a discrete fracture network and then compare this to the microseismic response before using that network, via up-scaling, in a dual-porosity simulation. This approach is extended by Du et al. (2010) with the inclusion of methods to further constrain the dual porosity

Figure 11 – ESV for Stage 1, 2, 3, and 4: Example Case History (adapted from SPE 110562)

Stage 1 - Yellow

Stage 2 –Dark Blue

Stage 3 - Red

Stage 4 – Light Blue

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parameters by integrating fracture treatment data and ESV. Schepers et al. (2009) have considered a triple porosity approach and found similar sensitivities to the model choice as Zhang et al. (2009).

Other approaches

Analytical approaches to estimating production in gas shales have been proposed by Lewis and Hughes (2008), Mattar et al. (2008), and Al-ahmadi et al. (2010), and Decline Curve Analysis has been used by Jordan et al. (2009). Restrepo and Tiab (2009) present an analytic model based on an assumption of dentritic fractures, and support their work with numerical simulations of a similar geometry. Grieser et al. (2009) used estimated half-lengths and an assumption of transverse fracturing in numerical models that were history-matched by adjusting permeability and fracture conductivity. That study did not use a network of fractures in multiple orientations.

Reservoir Simulation: Microseismic Application and Optimization using the Example Case History Many methods have been proposed to analyze the production history from complex fractures. Three approaches are presented corresponding to three different reservoir simulation concepts. The history matching results and ultimate recovery predictions for the three approaches are compared. The three approaches are: dual porosity modeling based on the estimated stimulated volume (ESV) from microseismic; explicit modeling of the hydraulic fracture network determined using the Wire-mesh fracture simulator, constrained by the microseismic map; and explicit modeling of discrete fracture planes extracted from the microseismic map using the Quantitative Identification of Planes method discussed above.

By assuming the total volume of the network cannot exceed the slurry volume pumped, the number of degrees of freedom is reduced in the dual porosity history matching by setting the fracture-grid porosity so that the fracture volume is equal to the slurry volume. This means that there are two history matching parameters, fracture permeability and the fracture-matrix transfer (σv). The size of the stimulated region is constrained by the ESV (Figure 11).

The Quantitative Identification of Planes approach uses the Maximum Likelihood Estimate (MLE) of fracture planes from the method of Williams and Khadhraoui (2010). The fractures are modeled explicitly as 1 ft wide grid cells in a distorted corner-point grid. This type of model captures the orientations of the fractures explicitly, ensuring that production interference effects are handled accurately. The total fractured volume is again assumed to be equal to the slurry volume, so there is only one history matching parameter: the fracture conductivity. It should be noted that fracture porosity has a minor impact on the history match.

The results from the Wire-mesh simulation provide both fracture dimensions and an estimate of proppant placement. Both the propped and un-propped regions of the fracture network are modeled in the reservoir simulations. The fracture network is modeled using 1ft wide grid cells and geometric gridding is used between the fractures to properly model the pressure transients in the reservoir matrix. As in previous cases, the slurry volume is used to define the total fractured volume of the entire network, and additionally we use the total proppant volume to define the fractured volume of the propped region. In this approach there are two history matching parameters: the conductivities of the un-propped and propped regions.

The simulations are constrained using bottom-hole pressure (BHP) control, where the bottom hole pressures were calculated from tubing-head pressures using the two-phase flow correlation of Orkiszewksi (1969). The history matching parameters are adjusted until a match of both production profile and cumulative production over the life of the well is obtained. All the approaches successfully history-matched the observed production under the applied BHP control. The common input parameters are listed in Appendix 1. Using the history matching results, the estimated ultimate recovery was forecasted using a BHP of ~1200 psi, consistent with the end of the production history, for a 30-year forecast period. Table 3 summarizes the history matching results. The fracture spacing and fracture conductivity for the dual porosity approach were estimated from the dual

porosity parameters σv and kf (Appendix 2). Figure 12 compares the gas rates and cumulative gas for the history match cases and the actual production, while Figure 13 shows the 30-year

forecast for each case. The history matches from the three model approaches are similar, but the 30-year forecasts are significantly different.

The dual porosity results indicate a history-matched conductivity of 0.25 md-ft and a fracture spacing of 133 ft. The explicit fracture extraction with the discrete fracture planes identified using the MS mapping event pattern results match with a higher fracture conductivity (14 md-ft). The Wire-mesh case consists of a combination of linear fractures (stages 1 and 2) and fracture

Model σv Permeability (md)

Spacing (ft)

Conductivity (md-ft)

Dual porosity 0.00045 3 133 0.25 Frac Plane Extraction n/a 0.06 n/a 14 Wire-mesh complex fracture model

n/a 0.001 (un-propped) 0.049 (propped)

Table 2

0.03 (un-propped) 15 (propped)

Table 3 - History matching results, history match parameters are shown in bold

SPE 133877 17

networks (stages 3 and 4) with dimensions determined from fracture modeling (Table 2). The Wire-mesh model was assigned a very low fracture conductivity (0.03 md-ft) in the un-propped region and the history match indicated a low fracture conductivity (15 md-ft) in the propped region, similar to the fracture plane extraction approach.

The fracture conductivity values from the history match were all consistently very low, even in the propped region of the Wire-mesh model. The un-propped fracture conductivity values are consistent with the lab results presented by Fredd et al. (2001) which show fracture conductivity of aligned un-propped fractures falling below 0.1 md-ft at 2800 psi closure stress. Later in this paper, it is shown that improvements in fracture conductivity could significantly improve well performance.

Discussion of simulation results

The three concepts each provide a reasonable match to the production history, and their different predictions reflect the different assumptions made. The pressure distributions at the end of the history match and after 30-years of production are compared for each approach in Figure 14 and Figure 15. Although the quality of the history matches is not significantly different for the three approaches, the drainage patterns are different due to the fracture geometry. The history-matched dual porosity parameters are in reasonable agreement with the results from discrete simulation (Table 3), even though the early time rate is not captured by the dual porosity model and the estimated ultimate recovery is very different. The dual porosity approximation fails to capture early time rate behavior because it considers a simplified steady-state and does not accurately represent drainage from a discrete representation of the same geometry (Gurpinar and Kossack, 2000). The ultimate recovery is

limited by the extent of the ESV because the dual porosity model allows no flow between the matrix blocks and therefore the edge of the ESV ultimately acts as a boundary, whereas even for 100 nanodarcy permeability there is some drawdown beyond the original stimulated zone after 30 years of production (Figure 15).

The dual porosity model can be improved by allowing pressure drawdown within the matrix, which is accomplished by modeling the system using dual permeability, with a matrix permeability of 100 nanodarcy, allowing some matrix flow. The effect on the cumulative production is significant (Figure 14) and the drainage from this model shows similarities to the discrete model based on Wire- mesh (Figure 15). Therefore, a properly formulated dual porosity-ESV based approach can yield similar results to the more rigorous complex Wire-mesh fracture model approach. However the dual porosity history match parameters are not easily

used to improve future simulation designs because their relationship to treatment conditions is unknown.

Figure 12 – Reservoir simulation history matching comparison of three different approaches: Dual Porosity constrained by ESV, Fracture Plane Extraction, Complex Fracture Modeling

Figure 13 – Production forecast for history match cases

Fracture Plane Extraction

Wiremesh complex fracture model

Dual Porosity

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The Wire-mesh geometry has captured the observed early-time rate behavior best, and shows separate fractures at the toe with

something approaching a dual-porosity geometry at the heel. The production simulations show that high conductivity fractures provide the production to date, and the low conductivity network at the heel will provide the reservoir contact needed to support the long term production (Figure 15, middle row). The pressure distribution after 30-years for the Wire-mesh approach shows that drainage is primarily in the propped region of the hydraulic fracture or fracture network. This is an important finding, which suggests that increased propped fracture length could significantly improve well performance. This also highlights the importance of developing complex fracture propagation models that can estimate proppant distribution within complex networks and can be calibrated using MS data, providing an optimization tool that has been missing from shale-gas evaluation workflows.

The Maximum Likelihood Estimate of major planes from microseismic mapping yields a geometry with a small number of conductive fractures. This model contains the highest conductivity deep in the formation because the conductivity is assumed constant along the fracture length. The fractures do not interact strongly, so this geometry provides the highest prediction of ultimate production (Figure 15, bottom row). Both the fracture extraction and dual porosity-ESV approaches provide a method to extract fracture geometry using MS data, but neither provide estimates of the propped and un-propped regions and neither of these approaches incorporate treatment parameters (mass balance and fracture mechanics).

Comparison of the pressure distributions in Figure 14 and Figure 15 shows that the approach used to estimate fracture geometry from MS event patterns can significantly impact the predicted drainage patterns and therefore influence well spacing and placement. In this example, the dual-porosity and Wire-mesh approaches would indicate the need for closer well spacing compared to the fracture plane extraction approach, while the fracture plane extraction approach would suggest the need for more fracture treatment stages due to inefficiency of drainage between the sparse, but long discrete planar fractures. The Wire-mesh approach is probably the most representative of actual drainage, because it incorporates mass balance when estimating the fracture spacing and provides an estimate of proppant distribution that can be used to model the propped and un-propped regions in the reservoir simulation. Adding Value – Example Case History Although a complete optimization of the completion and stimulation design is beyond the scope of this paper, some potential improvements to the completion strategy and fracture treatment designs were investigated. Review of the reservoir simulation results and drainage pattern based on the Wire-mesh model for this example suggested that production may be limited by propped length, fracture conductivity, and the number of stages in the toe of the lateral where fracture growth is planar. These three factors were investigated further.

Reservoir simulation with a stage added between Stages 1 and 2 showed an insignificant increase in long-term production. The Wire-mesh fracture model was used to investigate the effect of modifying the treatment schedule to increase the propped length in the toe stages. The injection rate was lowered and treatment volume was decreased by 25%, while the amount of 100-mesh sand was increased significantly and the pad decreased. This resulted in both an increased propped fracture length and reduced

Figure 14 - (left) The dual permeability model indicates that matrix permeability around the ESV affects final recovery. The pressure at late time (right) shows a very different drainage pattern to the dual porosity model

Wellhead

SPE 133877 19

.

Figure 15 - Left column shows reservoir pressure at end if history match, right shows the pressure after 30 years. Top row are pressure distributions for the dual porosity approach, middle are wire-mesh, and bottom are fracture plane extraction.

Dual Porosity – ESV Approach

Wire-mesh Complex Fracture Modeling Approach

Fracture Plane Extraction Approach

WellheadWellhead

WellheadWellhead

Wellhead Wellhead

Pressure (psi)

20 SPE 133877

treatment cost. In addition, the Wire-mesh fracture model showed that modifying the perforation strategy could reduce the overlap of Stages 4 into the volume stimulated by Stage 3, increasing the propped lengths in Stage 4. These two changes in propped length had a relatively modest effect, as shown in Figure 16. Increasing fracture length does not materially impact production when propped fracture conductivity is very low.

Figure 16 shows that increasing the conductivity of the propped fracture tenfold would dramatically increase initial production rates and improve 30-year gas recovery by 25%. The economic impact is significant, as first year gas production would double. Increasing the conductivity of the un-propped fracture network does

not impact initial well performance, but has a large impact on 30-year gas recovery. In the example case history, the overriding factor limiting production is propped and un-propped fracture conductivity,

consistently indicated by all three reservoir simulation approaches. Unfortunately, the reason for the low fracture conductivity in practice is not clear. It may be related to a combination of proppant settling, retention and gravity segregation of fracture fluid, and ultra low “average” proppant concentrations (i.e. proppant is actually transported through a large fraction of the fracture network resulting in very low concentrations over a very large surface area). Increasing fracture conductivity could significantly improve well economics, increase gas recovery, and impact well spacing and placement.

Summary The integration of microseismic measurements with complex fracture models can provide a much-needed constraint to the interpretation of complex fracture growth. With this constraint we now have a means to include fracture propagation and mass balance into the microseismic to reservoir simulation workflow. In addition, the introduction of complex hydraulic fracture models provides a much more quantitative method to improve fracture treatment designs and completion strategies. The application of complex fracture models is in its infancy and there is still an enormous learning curve before the models can be routinely used to make reliable decisions. However, complex fracture models, when combined with geomechanical models, microseismic measurements, and reservoir simulation can provide a more reliable evaluation of hydraulic fracture characteristics than previously possible.

The most important value of microseismic data is providing a constraint for subsequent production modeling. This constraint comes in a number of forms, including characterization of natural fractures, calibrating complex hydraulic fracture models, and providing a bound for the reservoir volume contacted by the hydraulic fracture (ESV or SRV). With these constraints, geomechanical, complex fracture, and reservoir simulation models can be used to more reliably estimate the spacing of the network fractures, the location of proppant within the fracture network, the distribution of conductivity within the fracture network, the drainage area, and gas recovery. With a more reliable estimate of the fracture network properties and a calibrated complex fracture model, treatment designs can be improved more systematically, with the goal of reducing the trial-and-error empirical approach currently employed in shale-gas development. The combination of microseismic data and geomechanical modeling can be used to calibrate seismic interpretations and provide more reliable predictions of stress variations and natural fracture characteristics, resulting in better placement of horizontal wells and fracture treatment designs specific to each environment. Completing the workflow and going from microseismic to reservoir simulation can result in improved well spacing, better drainage patterns, and increased recovery factor.

The example case history illustrated the application of microseismic measurements to improve seismic interpretations, resulting in a method to predict natural fracture orientation, stress anisotropy, and hydraulic fracture complexity using seismic interpretations. This could be a very powerful tool for improving well placement and spacing, while also providing a method to tailor stimulation designs and perforation strategies specific to each horizontal lateral. Microseismic data, combined with stress measurements from advanced sonic logs and geomechanical modeling, provides an important constraint to calibrate the complex hydraulic fracture model. Reservoir simulation history matching, constrained by the fracture geometry and conductivity profile from the complex fracture model, indicated that fracture conductivity was insufficient in both the propped and un-propped regions

Figure 16 - Impact of fracture design changes on well performance

Propped fracture conductivity x 10

Propped and un-propped fracture conductivity x 10

History match forecast

Improve propped fracture length

SPE 133877 21

of the fracture network – resulting in opportunities to improve well productivity in future completions. The calibrated complex fracture model was used to illustrate how proppant placement could be improved while reducing injection rate and total fluid volume. The production that could be expected from the improved designs was then forecasted using the calibrated reservoir simulation model, showing significant potential increases in gas rates if propped fracture conductivity can be increased. The reservoir simulation history match also provides important insights into drainage patterns that can be used to improve future well placement and spacing.

What’s missing?

The primary obstacle in the optimization process is the ability of the models to reliably predict the impact of changes in completion strategy and fracture treatment design. In the example case history the reservoir simulations assume a matrix permeability of 100 nD, which is common in the Barnett shale. However, matrix permeability is not well known in shale reservoirs and measurements are difficult, as standard core and well testing techniques are not applicable in nano-Darcy permeability environments. Although the results of the example case history appear reasonable, the uncertainty could be reduced with more accurate measurements of matrix permeability – a common problem in most shale-gas reservoir modeling. In addition, it is frequently difficult to reliably determine an accurate description of the natural fracture characteristics (spacing, distribution, orientation). More accurate discrete fracture network (DFN) models would help constrain the fracture and reservoir modeling. A key measurement that is often missing from reservoir simulation history matches in horizontal shale-gas completions is zone-by-zone production rates. Periodic production logs could provide an estimate of the production profile for each perforation cluster and significantly reduce the uncertainty of the reservoir simulation history match.

Although we routinely lack all the data necessary to constrain the reservoir simulation history match, the integration of microseismic, log, geological and seismic data with complex hydraulic fracture propagation models and geomechanical models can result in much more reliable evaluations of well performance. The power of integration is the constant iteration between sparse measurements and imperfect models to ensure a self-consistent result (that satisfies all the data and models). The learning curve should accelerate steeply as the models continue to improve and measurements become more comprehensive and frequent.

Acknowledgement The authors would like to thank Schlumberger for permission to publish this paper. The authors would like to especially thank Devon Energy for the example case history. We would also like to thank Hongren Gu, Olga Kresse, Charles Cohen, and Ruiting Wu for their contributions to the Wire-mesh and UFM models and assistance with the fracture simulations. Nomenclature

a = major principal axis for Wire-mesh model, L b = minor principal axis for Wire-mesh model, L bpm = barrels/min, L3/t DFN = discrete fracture network dx = fracture spacing “x” direction, L dy = fracture spacing “y” direction, L ESV = estimated stimulated volume, L3 h = net pay, and also fracture height, L k = permeability lb, Klb = pounds, 1,000 pounds, M md = 10-3 Darcy, L2 MS, MSM = microseismic, microseismic mapping nd = 10-9 Darcy, L2 p = pressure SRV = stimulated reservoir volume, L3

xf, ,xf,x xf,y = hydraulic fracture wing or half-length, L(x and y subscript denotes x,y-directions in network) Δσ = horizontal stress anisotropy, σH - σh, F/L2 Δxx = orthogonal fracture spacing, L σh = minimum horizontal stress, F/L2

σH = maximum horizontal stress, F/L2

σtotal = maximum horizontal stress, F/L2

σv = vertical stress, F/L2 and also dual porosity fracture-matrix transfer function

22 SPE 133877

SI Metric Conversion Factors ft x 3.048 e-01 = m psi x 6.894 757 e+00 = kPa

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Appendix 1 – Reservoir properties for simulation models

top depth 6900 ft reservoir thickness 400 ft reservoir pressure 3800 psi porosity 3% permeability 100 nd water saturation 30% gas gravity 0.6

Appendix 2 - Estimating Fracture Spacing and Fracture Conductivity from Dual Porosity Modeling Parameters In the dual porosity model, it is assumed that the fracture network itself shows some homogeneity of fracture properties and spacing, so that a continuum average behavior captures the behavior of the network. To compare history matched results to the explicit fracture models we consider Kazemi’s approximation (Kazemi, 1976) for the matrix-to-fracture transfer. From this we may recover approximate fracture spacing, width and conductivity: 4 1 1 1

where Lx, Ly and Lz are the matrix block sizes (and therefore fracture spacing) in each of the principal directions. By analogy to the Wire-mesh model we consider two directions (Lz → ∞) and will assume Lx=Ly. A 50 ft spacing (as was used in the Wire-mesh simulations) corresponds to: 4 250 0.0032

The fracture width, if the width is very small compared to the spacing, can be approximated as porosity multiplied by Lx, and

the conductivity similarly approximated as fracture permeability multiplied by width.

From the dual porosity results we can perform the reverse estimation: 8 80.00045 133 3 0.000625 133 0.25