SPE 146876

Seismic-to-Simulation for Unconventional Reservoir Development C.L. Cipolla, T. Fitzpatrick, M.J. Williams, and U.K. Ganguly, Schlumberger Copyright 2011, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Reservoir Characterisation and Simulation Conference and Exhibition held in Abu Dhabi, UAE, 9–11 October 2011. 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 The completion strategy and hydraulic fracture stimulation are the keys to economic success in unconventional reservoirs. Therefore, reservoir engineering workflows in unconventional reservoirs need to focus on completion and stimulation optimization as much as they do well placement and spacing. This well-level focus requires the integration of hydraulic fracture modeling software and the ability to utilize measurements specific to unconventional reservoirs. This paper details a comprehensive integration of software, data, and specialized measurements specific to unconventional reservoirs that allows efficient full-cycle seismic-to-simulation evaluations.

It is very important to properly model hydraulic fracture propagation and hydrocarbon production mechanisms in unconventional reservoirs, a significant departure from conventional reservoir simulation workflows. Seismic-to-simulation workflows in unconventional reservoirs require hydraulic fracture models that properly simulate complex fracture propagation which is common in many unconventional reservoirs, algorithms to automatically develop discrete reservoir simulation grids to rigorously model the hydrocarbon production from complex hydraulic fractures, and the ability to efficiently integrate microseismic measurements with geological and geophysical data. The introduction of complex hydraulic fracture propagation models now allows these work-flows to be implemented.

This paper documents an efficient, yet rigorous, integration of geological and geophysical data with complex fracture models, single-well completion and stimulation focused reservoir simulation, and microseismic measurements. The implementation of a common software platform and the development of specialized gridding algorithms allow complex hydraulic fracture models to be calibrated using microseismic measurements in the context of local geology and structure. The complex hydraulic fracture geometry, including the distribution of proppant, is automatically gridded to a common Earth Model for single-well reservoir simulation. The software platform, newly developed complex hydraulic fracture models, and automated gridding algorithms are illustrated in a case history from the Barnett Shale unconventional gas play.

Introduction The primary difference between seismic-to-simulation workflows for unconventional reservoirs compared to conventional reservoirs is the scale of the simulation; unconventional reservoir simulation is focused on the well and the specifics of the completion (i.e. – the hydraulic fracture treatments), many times with a very detailed geologic description. Conversely, seismic-to-simulation workflows for conventional reservoirs focus on large-scale reservoir behavior (e.g. - multi-well and full field simulation models) and typically “up-scale” the fine details of both the completion and reservoir heterogeneities such as natural fractures. Although upscaling of some reservoir properties may still be required when modeling unconventional reservoirs, the hydraulic fracture must be rigorously modeled. Without coupling the hydraulic fracture geometry and conductivity with subsequent well performance, it can be difficult to evaluate well performance and improve future completions (Mayerhofer et al. 2006, Cipolla 2009). The “seismic” portion of the seismic-to-simulation workflow for unconventional reservoir is also focused much more on the well-scale variations than large-scale field-wide variations, as local variations in structure and rock properties can significantly affect hydraulic fracture growth, stress regime, natural fracture distribution and orientation; all of which can dramatically impact well performance.

The vast majority of unconventional reservoirs in North America are developed using horizontal wells with multiple hydraulic fracture treatments (King 2010). This approach maximizes reservoir contact and minimizes the surface “footprint”. These completions typically consist of 10-20 propped fracture treatment “stages”, with each stage containing 2-8 perforation “clusters” designed to promote multiple fracture initiation points. Although stimulation treatment designs, number of stages, and number of perforation clusters per stage differ considerably, most completions in shale reservoirs require several millions

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of gallons of water and several million pounds of proppant. The focus of this paper will be the seismic-to-simulation workflow for horizontal completions in shale reservoirs. The seismic-to-simulation workflow for unconventional reservoirs is illustrated in Fig. 1. The boxes with green shading highlight key components that, until recently, have been absent from this workflow. This paper will detail how the introduction of 3D complex hydraulic fracture models and automated reservoir simulation grid generation has enabled the seismic-to-simulation workflow in unconventional reservoirs. Improving completion efficiency is a key component in the unconventional reservoir workflow. Miller et al. [2011] show production log results that illustrate that completion efficiency may be low in many shale wells, with an average of 30% of the perforation clusters unproductive. The details of the Completion Advisor are discussed by Cipolla et al. [2011c] and beyond the scope of this paper. The primary limitation to implementing this integrated workflow has been the absence of a common software platform that allows easy access to data and sharing of results. This limitation is being eliminated with the introduction of software platforms where geologists, petrophysicists, geophysicists, and engineers can access and share information. This allows seamless workflows from seismic evaluation and Earth Modeling to fracture modeling to reservoir simulation. The input data to build these models are easily accessible, while calibration of these models is facilitated through the efficient integration of microseismic monitoring and production data, all via the common software platform.

The first step is to develop a detailed Earth Model in the vicinity of the well. This requires the integration of wellbore measurements (e.g.-logs, cores, and drill cuttings) and larger scale seismic and geological data. Wellbore measurements provide important details of the lateral variations in reservoir and rock properties, including in situ stress (Baihly et al. 2010). Once the completion strategy (i.e. – fracture treatment staging, location of perforation clusters, and number of perforations in each cluster) is finalized, fracture treatments must be designed for each stage. This requires fit-for-purpose hydraulic fracture propagation models for each environment. These models must adequately capture the physics of hydraulic fracture growth specific to each geological setting and they should be calibrated using microseismic monitoring (MSM). In many tight gas environments planar hydraulic fracture models may be adequate (Mayerhofer et al. 2000, Peterman et al. 2005), but even in these relatively simple environments the models should be calibrated using microseismic data (Weijers et al. 2005). In unconventional reservoirs such as shale, hydraulic fracture growth can be very complex, requiring much more sophisticated models to adequately capture the interaction between the hydraulic fracture and natural fractures (Weng et al. 2011). Fracture model calibration using microseismic data is even more important in these complex environments (Cipolla et al. 2011a).

SeismicStructure

Natural Fractures, FaultsRock PropertiesStress Variations

Earth Models

1D or 3D MEMReservoir ModelGeological Model

DFN

Logs, Core & Petrophysics

Natural FracturesRock Properties

Stress Profile/ AnisotropyReservoir Properties

Hydraulic Fracture Models

3D Complex – Orthogonal(wire‐mesh)

3D Complex – DFN(UFM)

Reservoir Simulation Models

Single Well ConventionalSingle Well – Next Gen.Multi‐Well Sector Models(Conventional/Next Gen.)

Automated Grid Generation

OrthogonalUnstructured

Hydraulic Fracture Diagnostics

Microseismic Mapping

Completion Advisor

Perforation Locations Staging Strategy

Fig. 1 – Seismic-to-Simulation workflow for unconventional reservoirs. The boxes highlighted in green indicate new developments to enable the seismic-to-simulation workflow for unconventional reservoirs.

In addition to hydraulic fracture models, fracture treatment design requires production forecasts to evaluate the impact of treatment parameters on well performance (e.g. – proppant type, size, and amount; fluid type and volume; injection rate). Discrete gridding the hydraulic fracture in a numerical reservoir simulation model is the most robust and flexible approach, but can be a time consuming and cumbersome exercise if the process is not automated. Automated gridding of multiple planar hydraulic fractures in horizontal wells has been possible for many years (Shaoul et al. 2005), but the application of

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these integrated approaches has been limited (Olson et al. 2003, 2004, Lolon et al. 2007). However, the integration of complex hydraulic fracture models with numerical reservoir simulation has been a manual and time-consuming process, with limited application (Cipolla et al. 2010). Newly developed algorithms for automated gridding of complex hydraulic fractures in numerical reservoir simulation models are presented in this paper that now allow the efficient application of the “complex hydraulic fracture modeling to reservoir simulation” portion of this integrated workflow (Fig. 1). As with hydraulic fracture models, the reservoir simulation models need to be calibrated using actual production data to ensure the production forecasts are reliable. This calibration process is intimately linked to understanding hydraulic fracture performance; specifically fracture conductivity, effective fracture surface area, and stimulated volume (Mayerhofer et al. 2008, Cipolla et al. 2008).

Microseismic Monitoring Before discussing the seismic-to-simulation workflow for unconventional reservoirs, a brief overview of microseismic monitoring (MSM) is provided since this technology has become an important measurement to characterize hydraulic fracture growth and is an important component of the overall workflow. Additional details concerning microseismic monitoring are discussed by Warpinski [2009b], Maxwell [2009b], and Maxwell et al. [2010]. MSM is the most widely used technology to measure hydraulic fracture geometry because it provides the most complete picture of hydraulic fracture growth. In excess of 5000 fracture treatment stages were monitored using microseismic monitoring in 2010, with more than 20,000 treatments mapped in the last 10 years. Microseismic monitoring has added immensely to our understanding of fracture propagation, especially in unconventional reservoirs such as shale (Fisher et al. 2002 and 2004; Maxwell et al. 2002 and 2009a; Daniels et al. 2007; Le Calvez et al. 2007; King et.al. 2008; King 2010; Warpinski et al. 2008; Vincent 2009; and Waters et al. 2009). Microseismic monitoring, which is a straightforward application of earthquake seismological principles, consists of the detection, location, and further analysis of extremely small seismic events induced by the fracturing process (Albright and Pearson 1982; House 1987).

Fig. 2 – Basics of Microseismic Monitoring. The P- and S-wave sonic velocity model, combined with a downhole array of seismic sensors in a nearby offset well that record microseismic events, allows the location of the event to be determined. The distance to a given event is constrained by the separation of the P- and S-wave arrival times, while the azimuth-angle is determined using the polarization of P- and/or S-wave signals (hodogram analysis). The depth of each event is constrained by analyzing the arrival times on multiple seismic sensors (move out).

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Fig. 2 illustrates the basic principles of MSM. Typically, arrays of multiple receivers are positioned over a 300 to 1,000 ft (~100m to 300m) vertical interval in a single nearby offset well. The microseismic events are usually not detectable as discrete events at the surface but only from other wellbores within about 3,000 ft (~900m) of the fracture treatment well. The interpretation of MSM is discussed by Warpinski et a1. [2004] and Cipolla et al. [2011b]. The typical interpretation of microseismic event patterns consists of six primary observations:

1. Fracture Length. 2. Fracture Height. 3. Fracture Azimuth. 4. Fracture Complexity (i.e. – network or planar fractures). 5. Fracture Location with respect to the perforations, frac port, or other exit point from the wellbore. 6. Anomalous behavior (i.e. – fault activation, asymmetry, etc.).

The integration of microseismic measurements with other measurements is discussed by Warpinski [2009a], Cipolla et al.

[2010], and Ejofodomi et al. [2011]. The role of microseismic monitoring in the seismic-to-simulation workflow for unconventional reservoirs will be highlighted throughout this paper. Seismic, Geology, and Earth Model Development Seismic and geology play a significant role in the development of a well-scale Earth Model that is required for modeling production from unconventional reservoirs (Fig. 3). Advanced seismic characterization can provide a detailed well-scale measurement of structure and faulting and in some cases provide insights into rock properties and natural fractures. Well-

scale measurements such as logs, core, and drill cuttings are required to provide the necessary detail to characterize rock mechanical properties, stress variations, and the distribution and orientation of natural fractures. This information can be used to develop discrete fracture network (DFN) models to describe natural fractures and detailed 3D geomechanical models. The addition of microseismic monitoring provides a key measurement to constrain the DFN and geomechanical model, while also providing information that can be used to correlate hydraulic fracture growth with larger scale seismic characterization. The details of the seismic, geological, geomechanical, and petrophysical data and interpretations required to implement the workflow shown in Fig. 3 are beyond the scope of this paper. For reference, Bailhy et al. [2010] and Ejofodomi et al. [2011] show examples of the integration of both well-scale measurements and larger scale seismic and geological characterization to evaluate well performance in unconventional reservoirs. This paper will focus on the integration of seismic and geological data with complex hydraulic fracture models, microseismic measurements, and reservoir simulation for unconventional reservoir applications.

DFN Geomechanical ModelSeismic and Geology Microseismic Monitoring

Fig. 3 – Seismic and Geology, with the addition of microseismic measurements, provide the essential starting point for natural fracture characterization and development of both well-scale and field-scale Earth Models.

Example of Integration of Microseismic with Well-Scale Seismic Interpretation (after Maxwell et al. 2011) Microseismic monitoring was performed on three horizontal well hydraulic fracture stimulations in the Upper Montney formation in NE British Columbia, Canada. The Montney is a fine to very fine grained siltstone, with matrix porosities between 3% and 6% and permeabilities in the 0.001 to 0.05 md range. The target interval is approximately 1750 m TVD, and each well was stimulated with between 6 and 8 stages of N2 energized slick water pumped into single perforation clusters over the length of the approximately 1500 m horizontal section, at rates of approximately 10 m3/min. The three fracs were monitored with a single vertical observation well in the center of the wells, instrumented with 8 – three-component seismic sensors. The monitoring well was close to one end of each of the fracs and the offset to the furthest stages was approximately 1600 m.

Fig. 4 shows a map view of the microseismicity recorded for all the stages. The distribution of the microseismicity follows a zone trending NW-SE approximately parallel to wells B and C, with very few events occurring to the NE. Norton et al. (2010) described how seismic reflection amplitude versus offset (AVO) data was used to invert for variations in Poisson’s ratio (PR) in the reservoir. There are significant lateral variations in PR between 0.1 and 0.3 through the region, as shown by contour lines in Fig. 4. While the processing explicitly inverted for PR, the variations can be considered indicative of general changes in material properties. The microseismic events can be seen to cluster in a NW-SE trend near wells B and C,

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defining a NE boundary to the microseismically active zone characterized by increased concentration of relative large magnitude microseismicity. Away from this NE boundary, the majority of the stages are characterized by NE-SW lineations.

The complex microseismic patterns and large magnitude events in the northeast portion of Fig. 4 were interpreted as fault activation, resulting in more complex fracture growth into pre-existing faults. The more linear microseismic event patterns in the southwest portion of Fig. 4 were interpreted as relatively simple planar fracture propagation. There is also a clear preference for the hydraulic fractures to propagate toward or within areas of the reservoir that exhibit a low Poisson’s ratio (yellow and orange shade in Fig. 4). Low Poisson’s ratio is typically associated with lower in situ stress.

The ant tracking algorithm further supports the interpretation of fault activation in this area as shown in Fig. 5. In this display, a clear fault occurring below the reservoir is depicted from the highlighted lineation. Fig. 6 shows a seismic section with the ant tracking, where the fault can be clearly seen through the vertical throw at the strong deeper reflection event. The fault is interpreted to branch into vertical and sub-vertical segments based on more subtle lateral changes in the reflectors above the marker. At the depth of the shallower reservoir reflection event, there is not as much evidence of throw although there is an anomalous local change in the reflected amplitude that could be related to the extension of the fault. This seems to be related to a decrease in the throw of the fault becoming below the reflection seismic resolution, such that it is not possible to say if faulting occurred at the reservoir depth. It should be remembered that ant tracking searches for faults with an apparent throw, implicitly assuming strain release associated with earlier tectonic movements. Faults with

small amounts of throw and potentially sub-seismic resolution are more likely to be still tectonically loaded and prone to releasing seismic energy due to effective stress changes and lubrication associated with hydraulic fracturing. Therefore, the microseismically-activated fault systems may be sub-seismic and hence difficult to resolve with seismic reflection data.

Well A Well B

Well C

Monitoring Well

Well A Well B

Well C

Monitoring Well

Fault activation and complex fracturing

Simple, Planar Fracture Growth

Fig. 4 - Microseismicity recorded during multiple stages of three wells, and Poisson’s ratio contours (hot colors are low values). Reference Maxwell et al. [2011], SPE 144207.

Fig. 5 - Microseismic events and ant tracking image. Reference Maxwell et al. [2011], SPE 144207.

There is also significant overlap of the microseismic event patterns from well-to-well, indicating that portions of the reservoir have been re-stimulated (i.e. – stimulated more than once). This overlap of the hydraulic fractures resulted in production/pressure interference between the three wells.

Example Summary The main aspects of the heterogeneous microseismic

distribution and integration with seismic interpretations are related to:

1. fault activation, 2. preferential growth towards low stress regions, and 3. re-stimulation of previously stimulated regions of

the reservoir. In this example the reservoir simulation portion of the “seismic-to-simulation” workflow is not available, which will provide much better understanding of the reservoir drainage associated with each treatment stage. In this case the drainage will obviously be controlled by the fracture

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dimensions. The microseismic responses can thus be used to improve staging, by potentially using closer spaced stages/perforation clusters. Well spacing can also be improved to avoid interference between parallel wells. Better understanding of the association between the microseisimicity and reservoir characterization has also allowed better well placement based on an ability to qualitatively predict the expected fracture geometries. However, quantitative geomechanical prediction of the fracture heterogeneity has yet to be performed.

The hydraulic fracturing of these three wells resulted in significant differences in the microseismic response. Interaction of the hydraulic fractures with a pre-existing fault resulted in relatively large magnitude microseismicity along the NE edge. In some cases, relatively simple, planar hydraulic fractures were created in the expected NE-SW direction, although the fractures tend to preferentially grow towards the SW in the direction of lower Poisson’s ratio where material properties and stresses are different. Qualitative learnings from the integration of microseismic and the reservoir characterization have been used for subsequent well placement decisions, and have led to improved well performance. Well placement and spacing are now being improved using seismic information, targeting areas of lower PR and avoiding proximity to faults. Wells are being drilled in the center of regions with low Poisson’s ratio to promote more symmetrical fracture growth. Closer perforation clusters have been used to increase reservoir contact and well spacing has been increased to mitigate well interference. The integrated investigation of the hydraulic fracture response provides the context for these decisions, illustrating the value of integrated reservoir characterization.

Fig. 6 - Ant tracking volume and corresponding seismic section showing two interpreted faults (red dashed lines). Right side is zoomed in region showing relationship with microseismic. Reference Maxwell et al. [2011], SPE 144207

Complex Hydraulic Fracture Models, Automated Grid Generation, and Reservoir Simulation The link between seismic interpretations, geology, and Earth Modeling and well performance for unconventional reservoirs is the hydraulic fracture. The ability to model complex hydraulic fractures and automatically generate a reservoir simulation grid enables this workflow (which was previously not possible). Fig. 7 illustrates the continuation of the seismic-to-

Complex Hydraulic Fracture ModelsMicroseismic Monitoring Reservoir Simulation Models

Fig. 7 – Continuation of the Seismic-to-Simulation workflow. After developing an Earth Model (Fig. 3), microseismic measurements are used to calibrate complex fracture models and the predicted fracture geometry automatically imported into the reservoir simulation model.

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simulation workflow. Microseismic measurements are used to calibrate complex hydraulic fracture models; a reservoir simulation grid is automatically generated that accurately represents the complex fracture geometry and conductivity distribution and also the overall reservoir properties, layering, and structure. In the seismic-to-simulation workflow, microseismic measurements are used to constrain the DFN and Earth Model (Fig. 3), as well as the complex hydraulic fracture model.

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 propagation. However, these planar models are inadequate for simulating complex fracture geometry in shale gas. With the recent introduction of complex fracture propagation models, the ability to model hydraulic fracture growth in complex geologic environments typical of many unconventional reservoirs is now possible. The details of the wire-mesh model and unconventional fracture model (UFM) featured in this paper are provided by Xu et al. [2009, 2010] and Weng et al. [2011], respectively. The two models differ significantly in their approach to modeling complex fracture growth, with the simpler wire-mesh model approximating the complex fracture network using an orthogonal set of fractures, while the more sophisticated UFM honors the natural fracture characteristics (DFN and mechanical properties). Calibrating complex hydraulic fracture models using microseismic measurements is discussed by Cipolla et al. [2011a]. The calibration (using MSM) and subsequent application of these complex fracture models provides details of the hydraulic fracture structure and the distribution of proppant within this structure (Fig. 8).

The complex fracture geometries and proppant distributions predicted by the complex fracture models present a significant challenge for reservoir simulation. Prior to the introduction of complex fracture models, reservoir simulation approaches have utilized dual porosity solutions to approximate the details of the fracture network (Du et al. 2009, 2010 and Mongalvy et al. 2011). However, the ability to discretely grid complex hydraulic fracture geometries in reservoir simulation models is now required to provide the necessary detail to properly evaluate fracture treatment designs and well performance.

There is no direct “link” between the fracture treatment design and the subsequent production prediction without discretely gridding the complex hydraulic fracture geometry in the reservoir simulation model. The “link” between fracture geometry and well performance must also extend to the calibration phase of the workflow, where actual well performance is history matched. The primary obstacle in this portion of the workflow is the ability to efficiently generate the complex reservoir simulation grid that accurately represents the hydraulic fracture.

Automated Grid Generation for Complex Hydraulic Fractures

In conventional reservoirs permeability is “high”, transient production behavior is short-lived, and modeling well-to-well interference is a primary objective of reservoir simulation. However, permeability is extremely low in unconventional reservoirs, transient production behavior is long-lived (years), well-to-well interference is a secondary issue, and modeling the

hydraulic fracture is the primary objective of the reservoir simulation. Therefore, it is important to capture the transient behavior of pressure/flow and accurately model flow regimes in the reservoir and hydraulic fracture. This requires very small grid blocks in the vicinity of the hydraulic fracture and wellbore, with the hydraulic fracture width typically being represented using 1 ft wide grid blocks and logarithmic grid spacing away from the fractures to properly model the pressure distributions and transients.

Pay‐zone 1

Pay‐zone 2

No pay

No pay

No pay

Side or Depth View – Single Fracture Segment

Plan View – Complex Fracture

Fig. 8 – Illustration of complex hydraulic fracture geometry. Plan view (lower illustration) shows the proppant distribution, with green, red, and yellow representing different concentrations/types of proppant and blue representing the un-propped region of the fracture network. The upper illustration shows the vertical distribution of proppant within a one of the network fractures. The lateral and vertical distribution of proppant and un-propped regions predicted by the complex fracture model must be accurately represented in the reservoir simulation model.

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In addition to generating a very detailed reservoir simulation grid that properly models the complex hydraulic fracture geometry, the distribution of fracture conductivity must also be accurately represented. Fracture conductivity is defined as the width of the fracture multiplied by the permeability of the fracture, in a given fracture segment. As depicted in Fig. 8, the complex hydraulic fracture model will provided a detailed prediction of the location and concentration of each proppant type within the fracture network. These lateral and vertical variations in proppant type and concentration must be translated into fracture conductivity variations and accurately mapped to the reservoir simulation grid. The geomechanical effects of stress changes along the hydraulic fracture on the fracture conductivity must also be properly modeled. Closure stress on the hydraulic fracture can vary dramatically due to changes in flowing pressure, production rate, and reservoir pressure. Closure stress is defined as the pressure within each fracture grid block minus the local in situ stress normal to the fracture (i.e. – stress that is acting to “close” the fracture). Increasing closure stress reduces fracture conductivity, but the effect will vary for each proppant type. In addition, the effect of closure stress on the un-propped regions of the fracture network must also be modeled, as even un-propped fractures may have sufficient conductivity impact production (Cipolla et al. 2008). The geomechanical effects in the hydraulic fracture are typically modeled using compaction tables for each proppant type and for the un-propped region, calculating the permeability in each fracture grid block every simulation time-step. The compaction tables are derived from laboratory data that is readily available for most proppants.

Although in low permeability reservoirs fluid velocity in the matrix is typically low, fluid velocity within the hydraulic fractures can be very high, especially near the wellbore. The high fluid velocity can result in significant non-Darcy flow effects that must be properly modeled in the reservoir simulation (Holditch and Morse, 1976; Olsen et al., 2004). Non-Darcy flow can significantly increase pressure drop within the fractures. The effect of Non-Darcy flow will vary depending on the proppant size and type, pressure, closure stress, production rate, and location within the fracture network. The parameters required to model non-Darcy flow are available for most proppants and must be properly “mapped” to the reservoir simulation grid for each proppant. Generating these complex reservoir simulation grids and accurately populating the hydraulic fracture properties is an overwhelming process if done manually. Although industry solutions for automatic grid generation for multiple planar hydraulic fractures in a horizontal well are available (Olsen et al. 2003, 2004; Shaoul et al., 2005), solutions for complex hydraulic fractures have been absent until recently.

Automated Gridding Algorithms Fig. 9 illustrates the varying degrees of hydraulic fracture complexity that must be addressed when developing automated

reservoir simulation gridding algorithms. As discussed, solutions for planar hydraulic fractures (Fig. 9a) have been available for many years and are relatively straight forward to implement. Two automatic grid generation algorithms have been developed, driven by the type of complex hydraulic model that is use to generate the fracture geometry. Models such as the wire-mesh assume orthogonal sets of hydraulic fractures with constant spacing between the fractures in each orientation (Fig. 9b). This type of complex fracture geometry can be modeled using structured orthogonal reservoir simulation grids and

classical reservoir simulation software. Models such as the UFM predict much more complex hydraulic fracture geometries that are dictated by the distribution of natural fractures (Fig. 9c) and cannot be modeled using structured orthogonal grids, requiring more sophisticated un-structured grids. In addition, these un-structured, very small and detailed grids can result in excessive simulation run-times for classical reservoir simulation software, requiring much more efficient reservoir simulators. Therefore, automated gridding algorithms have been developed for orthogonal hydraulic fractures that can be used in conjunction with classical reservoir simulation software and also for un-structured complex fractures that require “next generation” reservoir simulators that utilize parallel processing and other advancements. Fig. 10 shows an example of an un-structured reservoir simulation grid for a complex hydraulic fracture. The hydraulic fracture is represented by 1-ft wide

Horizontal well

P lanar Hydraulic  Fractures

Perforations

Complex  “Un‐structured” Hydraulic  Fr acturesComplex “Orthogonal” Hydraulic Fracture Network

(a) (b) (c)

Fig. 9 – Illustration of various fracture geometry models in a horizontal well completion. (a) Simple planar fractures (classical industry models), (b) complex fracture networks, assuming orthogonal fractures (wire-mesh), and (c) complex fracture networks controlled by the distribution of natural fractures (UFM).

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cells, with very fine girds surrounding the hydraulic fracture. The permeability and porosity in the 1-ft wide fracture cells are scaled to accurately represent the distribution of hydraulic fracture properties throughout the complex fracture network.

1 ft cell width for hydraulic fractures

~250 ft (75m)

~4000 ft (1220m) Fig. 10 – Example of un-structured reservoir simulation grid for a complex hydraulic fracture. The hydraulic fractures are represented by 1-ft wide cells in the reservoir simulation grid. The “hot” colors (e.g.- orange) represent higher pressures, while the “cool” colors (e.g. – green) represent lower pressures. Note, the red shading in the expanded region is used to highlight the hydraulic fractures and does not represent pressure in the fractures.

There are numerous technical challenges to overcome when developing automated gridding algorithms for complex hydraulic fractures, including proper, yet efficient, representation of fracture intersections, closely spaced fractures, and overlap of fracture networks. The algorithms must be efficient and produce a grid that accurately represents the complex hydraulic fracture. In addition, the reservoir simulation grid should optimize execution time, balancing the required accuracy with efficiency. The details of the algorithms will be presented in a separate publication and are beyond the scope of this paper. An example application of the “orthogonal” and “un-structured” automated gridding algorithms will be detailed later in the paper. Additional details of the automated gridding routines are provided in Appendix A. Seismic-to-Simulation: Barnett Shale Case History This case history builds upon the work of Cipolla et al. [2010], Daniels et al. [2007] and Rich and Ammerman [2010]. A horizontal Barnett shale completion is used to illustrate the seismic-to-simulation workflow for unconventional reservoirs. This well has a rich dataset, including microseismic monitoring for all stages of the stimulation, advanced sonic logs that provided estimates of minimum and maximum horizontal stress 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. Each treatment consisted of 25,000 bbls of water and 440,000 lbs of proppant. The fracture treatments were monitored using an array of geophones in an offsetting wellbore. The microseismic mapping results are shown in Fig. 11, illustrating the significant variation in microseismic behavior exhibited by stages 1

Stage  1, green

Stage  2, red

Stage  3, yellow

Stage  4, blue

σh from advanced sonic log

Fig. 11 – Microseismic monitoring data for Barnett shale horizontal well. Colors indicate microseismic events for each of the four fracture treatment stages. An advanced sonic log was also available for this well, providing information concerning on the well-scale variations in in situ stress.

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and 2 compared to stages 3 and 4. Stages 1 and 2 exhibit much more planar microseismic event patterns compared to stage 3 and 4, which exhibit very complex microseismic images.

The minimum horizontal stress (σh) from an advanced sonic log is also shown in Fig. 11. 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. 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. Fig. 12 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 the dominant natural fracture trends in the toe section of the lateral are parallel to the direction of hydraulic fracture propagation, while the dominant 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, and microseismic measurements provide a much more reliable understanding of differences in hydraulic fracture growth along the lateral and constrain the DFN and Earth Model. In this example, a detailed geologic description of the natural fractures was not available and the DFN was generated using a stochastic model guided by the advanced seismic interpretation and microseismic measurements (Fig. 13). A 3-

layer Mechanical Earth Model was developed using available logs and fracture treatment data. The Earth Model consisted of a single Barnett shale layer, with upper and lower non-productive and higher stress bounding layers. Minimum horizontal stress in the Barnett layer varied from 0.70 psi/ft in the toe-section of the lateral to 0.62 psi/ft in the heel section, while maximum horizontal stress ranged from 0.77 psi/ft to 0.65 psi/ft in the toe and heel, respectively. The difference in the two horizontal stresses varied from 0.10 psi/ft in the toe section of the lateral to 0.03 psi/ft in the heel.

Fig. 12 - Advanced seismic interpretation (from SPE 131779, after Rich and Ammerman [2010]).

Complex Hydraulic Fracture Modeling

Complex hydraulic fracture modeling was performed on each stage. The complex fracture models were calibrated using the microseismic measurements (reference Cipolla et al. 2011a for details of the calibration process). Both the wire-mesh and UFM models were used for this example. The Wire-mesh complex fracture model calibration results for stages 1 and 3 are shown in Fig. 14. The complex microseismic event pattern for stage 3 is adequately

Example horizontal 

well

Fig. 13 – Discrete Fracture Network (DFN) representing the distribution of natural fractures in the vicinity of the example horizontal well. Note that the dominant naturals trending NE-SW in the toe section of the lateral are absent in the heel section.

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represented using the Wire-mesh model; however, the more planar microseismic event pattern in stage 1 is difficult to match. Although not detailed in Fig. 14, the wire-mesh modeling showed that about 10% of the fracture geometry was propped, primarily near the horizontal well.

Fig. 15 shows the results using more sophisticated UFM for stages 1 and 3. The UFM utilizes the DFN (Fig. 13) and accounts for the interaction of the hydraulic fracture with the pre-existing natures fractures, while also honoring the variations in stress from the toe to the heel of the lateral. The UFM is capable of modeling both more planar stage 1 microseismic event pattern and the complex stage 3 pattern, consistent with the advanced seismic interpretation that helped constrain the DFN. The predicted proppant distributions are also shown in Fig. 15, illustrating that most of the proppant is located near the wellbore and that most of the fracture network is un-propped (less than 3% is propped).

The Wire-mesh model assumption of orthogonal fractures results in a much simpler fracture geometry for reservoir simulation purposes, but compromises on the detailed geologic context. Conversely, the UFM result is very irregular hydraulic fracture geometries dictated by the natural fracture distribution, with many of the hydraulic fractures “curving” due to interaction between the various fractures. The fracture geometry predicted by the UFM is much more difficult to accurately represent in reservoir simulation models, but honors the geology. Both models have their applications depending on the amount and quality of data available and the goals of the fracture and reservoir modeling.

Reservoir Simulation of Complex Hydraulic Fractures

The complex fracture geometry for each stage was discretely gridded and imported into the appropriate reservoir simulation model. As discussed, production from the Wire-mesh fracture geometry can be modeled using classical reservoir simulators, while the complex UFM geometries require next generation reservoir simulators. Fig. 16 shows the detailed reservoir simulation grid used to model the Wire-mesh complex fracture geometry predicted for the four fracture treatment stages, while also showing the propped and un-propped regions of the hydraulic fracture network. The grid consists of about 250,000 cells (single layer model). The history matching results using the Wire-mesh fracture geometry and orthogonal reservoir simulation grids are shown in Fig. 17. A very good match of the 1100-day production history was obtained using a matrix permeability of 0.0001 md, propped fracture conductivity of 15 md-ft, and 0.03 md-ft fracture conductivity for the un-propped regions of the fracture network. The history match indicated very low fracture conductivity in the un-propped region of the fracture network and lower-than-expected conductivity in the propped region. Realistic ranges for un-propped fracture conductivity are discussed by Mayerhofer et al. [2006] and Cipolla et al. [2008], while realistic range for propped fracture conductivity are presented by Vincent [2009]. The impact of fracture conductivity on well performance will be investigated later in the paper. Additional details of the reservoir simulation history match using the wire-mesh complex fracture modeling results are presented by Cipolla et al. [2010].

W ‐M  Stage  1

W ‐M  Stage  3

Fig. 14 –Wire-mesh (W-M) complex hydraulic fracture modeling results for stages 1 and 3. Microseismic measurements were used to calibrate the models (after Cipolla et al. [2011a]).

Gas production was forecast for 30-years using the history match parameters. Fig. 18 shows the reservoir pressure distribution after 1, 5, 15, and 30 years of production for the reservoir simulations using the Wire-mesh complex fracture geometry. The reservoir pressure distribution shows that production during the first 5-years is primarily from the propped

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regions of the fracture network near the horizontal wellbore (reference Fig. 16, right side graph). The un-propped portions of the fracture network begin to “slowly” produce gas after 15-years of production, but even after and 30-years of production gas drainage is dominated by the propped (higher conductivity) regions of the fracture network. The reservoir simulation clearly shows that the drainage area for wells completed in unconventional reservoirs is controlled by the effectiveness of the hydraulic fracture treatment, thus the need to focus the reservoir modeling on the well-scale as opposed to the classical field-scale approach that is applicable to conventional reservoirs.

Hydraulic fractures Propped regions

Un‐Propped regions

Plan view comparison to microseismic 3D view: geometry & proppantdistribution

Propped regions

Un‐Propped regions

Stage 3

Stage 1

Hydraulic fractures

Fig. 15 – Unconventional Fracture Model (UFM) results for stages 1 and 3. Microseismic measurements were used to calibrate the UFM (left). The proppant distribution in the complex facture network is shown on the ride side of the figure, indicating that most of the proppant is located near the wellbore and less than 10% of the fracture geometry is propped. Fracture surface area is 2,040,000 ft2 for stage 1, with 36,500 ft2 propped, ~ 2% of the stage 1 frac geometry is propped. Fracture surface area is 5,500,000 ft2 for stage 2, with 154,000 ft2 propped, ~ 3% of the stage 2 frac geometry is propped.

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Simulation Grid Hydraulic Fractures and Conductivity

Propped fractures (15 md‐ft)

Un‐Propped fractures (0.03 md‐ft)Microseismic events

Stage 1

Stage2

Stages 3 and 4

Fig. 16 – Reservoir simulation grid used to model Wire-mesh complex fracture geometry (left side) and detailed permeability distribution showing propped and un-propped regions of the fracture network (right side).

History Match 30‐year production Forecast

Fig. 17 – Production history matching results using the Wire-mesh complex fracture geometry (left graph); 30-year production forecast (right graph).

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1‐year 5‐years

15‐years 30‐years

~80 acres

Fig. 18 – Reservoir pressure distribution after 1, 5, 15, and 30 years for reservoir simulations using Wire-mesh complex fracture geometry.

Reservoir Simulation using Un-Structured Complex Fractures (UFM geometry) Stage 1 of the Barnett example is examined in more detail using the UFM to provide additional insights into the

relationship between fracture geometry, fracture conductivity, and well performance. The following reservoir simulations illustrate how fracture complexity and conductivity distribution affect gas production and drainage. The automated un-structured gridding algorithm was used to generate a reservoir simulation grid for the stage 1 UFM fracture geometry (Fig. 15). The details of the reservoir simulation grid are shown in Fig. 19, illustrating the un-structured gridding required to accurately capture the complex UFM fracture geometry. Fig. 20 shows the permeability distribution in the hydraulic fracture network, highlighting the propped and un-propped regions.

Gas production for stage 1 was forecast for 30-years using the same reservoir and fracture properties as the previous simulations (base case values): matrix permeability = 0.0001 md, propped fracture conductivity = 15 md-ft, and un-propped fracture conductivity = 0.03 md. The production forecast is shown in Fig. 21. The production forecast utilized next generation reservoir simulation software to achieve efficient execution times. It should be noted that the production and drainage patterns predicted using the UFM fracture geometry cannot be directly compared to the previous reservoir simulations that used the wire-mesh fracture geometry, as the hydraulic fracture geometry and distribution of fracture conductivity are very different. Therefore, the history matching results will also be different using the UFM geometry and will be discussed in another publication.

The pressure distribution after 30-years for stage 1 is shown in Fig. 22, illustrating that gas drainage is primarily in the vicinity of the higher conductivity propped fractures (refer to Fig. 20 for conductivity distribution). The reservoir simulation results also indicate that gas drainage is limited to the area or volume of the reservoir that was stimulated (i.e. – around the hydraulic fracture network).

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Fig. 19 – UFM un-structured reservoir simulation grids for stages 1.

Effect of Fracture Conductivity on Gas Production

The effect of fracture conductivity on gas production was examined by varying the propped and un-propped fracture conductivity from the base case values. The propped fracture conductivity was increased from 15 md-ft to 150 md-ft and also decreased to 1.5 md-ft to examine the effect of propped conductivity on gas production. In addition, the un-propped fracture conductivity was increased from 0.03 md-ft to 0.3 md-ft. The results are shown in Fig. 23, illustrating that increasing the conductivity of the un-propped regions of the fracture network is the key to improve gas production and recovery. Increasing propped fracture conductivity from 15 to 150 md-ft has little effect on gas production; however, if propped fracture conductivity is significantly lower than 15 md-ft then gas production is markedly reduced.

As noted in the previous sections, the complex fracture modeling results indicate that less than 10% of the fracture network is propped. Therefore, altering fracture treatment designs to prop more of the fracture network is likely a critical component of improve gas recovery from unconventional reservoirs. In addition, it is important that propped fracture conductivity is sufficient to maximize gas produced from the un-propped network fractures (Cipolla et al., 2008).

The pressure distributions after 30-years for the higher conductivity case (propped = 150 md-ft, un-propped = 0.3 md-ft) is compared to the lower conductivity case (propped = 1.5 md-ft, un-propped =0.03 md-ft) in Fig. 24. The figure illustrates the dramatic effect fracture conductivity has on gas drainage. Comparing (left side graph) to shows that increasing fracture conductivity significantly improves gas drainage, especially in the un-propped regions of the fracture network. Increasing fracture conductivity in the un-propped regions of the fracture network results in significantly lower pressures in the nano-Darcy matrix rock surrounding the un-propped fractures (Fig. 24, left side graph). Conversely, if the propped fracture conductivity is reduced from 15 to 1.5 md-ft, gas drainage is less efficient as

evidenced by the higher pressures around the propped fractures (right side graph in Fig. 24). These simulations emphasize the importance of characterizing the hydraulic fracture geometry while also understanding the distribution of fracture conductivity within this complex structure.

Un‐propped fractures

Propped fractures

Fig. 20 – Permeability in the fracture network, illustrating the distribution of fracture conductivity in propped and un-propped regions of the hydraulic fracture network.

Fig. 24Fig. 22

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Cumulative gas production

Gas production rate

Year

Fig. 21 –Stage 1 gas rate and cumulative gas production profile.

Summary of Barnett Shale Example The Barnett case history is an excellent

example of the seismic-to-simulation workflow for unconventional reservoirs. Well-scale seismic interpretations (curvature) were combined with microseismic mapping, advanced sonic logs, and regional geologic knowledge to characterized the variations in the in situ stress field and natural fractures in the vicinity if the horizontal well. These characterizations were used to develop and Earth model and DFN for subsequent calibration of complex fracture models using microseismic measurements. The complex fracture modeling revealed that the majority of the fracture geometry was not propped, while also providing the details of the hydraulic fracture structure.

The complex fracture geometry was then automatically gridded for reservoir simulation, accurately modeling the details of the fracture geometry and distribution of fracture conductivity within the complex fracture network. The reservoir simulation history matching identified low fracture conductivity as a primary factor affecting gas production and clearly showed that gas drainage is controlled by the size and effectiveness (i.e. – conductivity) of the fracture network. In this case, the reservoir simulations demonstrated that improving un-propped fracture conductivity could significantly increase gas production and drainage. In addition, the ability to link well-scale seismic interpretations to hydraulic fracture growth provides an important tool to predict fracture growth, improving well placement and allowing fracture designs to be customized using seismic-based estimates of in situ stress regime and natural fracture distribution.

Conclusions The seismic-to-simulation workflow for unconventional reservoirs is significantly different from its classical counterpart for conventional reservoirs. The scale of the workflow in unconventional reservoirs is much smaller, focusing on the well rather than the field. This well-scale focus is

required to properly evaluate and improve hydraulic fracture treatments, the “key” to unlocking the vast potential of unconventional resources. However, seismic-to-simulation for unconventional reservoirs was lacking two important technical components, (1) the ability to model complex hydraulic fractures and (2) tools to efficiently evaluate/forecast the production from these complex fractures. The recent introduction of complex hydraulic fracture models and next generation reservoir simulators combined with the automated gridding routines introduced in this paper has now completed this workflow. In addition to the technical advancements in fracture modeling and automated reservoir simulation grid generation, accommodating all the technical components (seismic, geology, geomechanics, microseismic, hydraulic fracturing, and reservoir simulation) in a common software platform now enables the efficient application of the seismic-to-simulation workflow for unconventional reservoirs.

Fig. 22 – Pressure distribution after 30-years of gas production for stage 1 UFM fracture geometry. Gas drainage is primarily from propped regions of the hydraulic fracture network and limited by the size of the network.

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Year

Propped = 150 md‐ftUn‐propped = 0.3 md‐ft

Propped = 150 md‐ftUn‐propped = 0.03 md‐ft

Propped = 1.5 md‐ftUn‐propped = 0.3 md‐ft

Propped = 15 md‐ftUn‐propped = 0.3 md‐ft

Fig. 23 – Effect of fracture conductivity on gas production for stage 1. Increasing fracture conductivity in the un-propped regions of the hydraulic fracture network significantly improves gas recovery. Lower conductivity in the propped region reduces gas production. The red text in the labels above indicates parameters that were changed from the based case values of propped =15 md-ft and un-propped = 0.03 md-ft.

Higher un‐propped fracture conductivity Lower propped fracture conductivity

Fig. 24 – Pressure distribution after 30-years of production when fracture conductivity in increased to 150 md-ft in the propped regions and 0.3 md-ft in the un-propped regions of the fracture network (left graph) and when propped fracture conductivity is decreased from 15 md-ft to 1.5 md-ft (right graph). The graphs illustrate the dramatic impact of fracture conductivity on gas production. Increasing the un-propped fracture conductivity significantly improves the drainage area of the well.

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Acknowledgement The authors would like to thank Schlumberger for supporting this work and for permission to publish this paper. Special thanks to Calum Byrom with Tessella Plc for his help with the reservoir simulations using un-structure grids. The authors would also like to acknowledge Shawn Maxwell for his support with the Montney case history and Xiaowei Weng and Charles Cohen for providing the UFM cases for the Barnett example. Nomenclature AVO = amplitude versus offset bbls = Barrels, L3 DFN = Discrete Fracture Network PR = Poisson’s ratio UFM = Unconventional Fracture Model W-M = Wire-mesh Fracture Model σh = minimum horizontal stress, M/LT2

σH = maximum horizontal stress, M/LT2

SI Metric Conversion Factors

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Appendix A - Modeling Unconventional Fractures using Unstructured Grids Many attempts have been made to accurately simulate flow from hydraulically induced fractures. For the most part these have used conventional hexahedral cells, either very fine to match the fracture and wellbore geometry, global Tartan grids or local grids with our without logarithmic cell spacing. Attempts have also been made to model with unstructured PEBI grids. These approaches are illustrated in Fig. A1 through Fig A5. Additional information concerning grid systems for hydraulically fractured wells and subsequent reservoir simulation are provided by Hayes et al. [1994], Gunasekera et al. [1997], and Banerjee at al. [2000]. The approach adopted in this paper is to model the geometry of a complex fracture network as closely as possible while producing a fit for purpose grid for use in a reservoir simulator tuned for solving such un-structured grid models.

The basic algorithm includes the following: • Intersect the mid-planes of a discrete Fracture Network to produce a 2D network • Replace the actual fracture thickness with a nominal value ( usually 1 ft) • Replace narrow angle (< 5 degree) intersections of fractures with a “dogleg” kink . This avoids tiny cells at

the intersection of the fractures that can lead to slower simulation times. • Create a Voronoi tessellation of this modified network • Create the corresponding dual grid; the so called Perpendicular Bisector or PEBI cells. • Extrude this grid through Z and honor any layering schemes on the parent grid • Create pore volume and transmissibility multipliers to compensate for the increase in fracture cell

thickness during the discretisation. • Sample the conductivity and fracture aperture on the Discrete fracture patches onto the simulation grid.

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  Fig. A1-: Explicit fracture modeling using nested Local grid Refinements

Fig. A2-: LGR with Logarithmic growth perpendicular to the fracture

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Fig. A3-: Global Tartan grid drawn around hydraulic Fracture

  Fig. A4-:Single plane Hydraulic fracture with linear refinement

SPE 146876 23

  Fig. A5-: Discrete Fracture Network intersected by well