[Society of Petroleum Engineers SPE Annual Technical Conference and Exhibition - New Orleans, Louisiana (2009-10-04)] SPE Annual Technical Conference and Exhibition - Evaluating Stimulation

SPE 124843

Evaluating Stimulation Effectiveness in Unconventional Gas Reservoirs Cipolla, C.L., Lolon, E.P., and Dzubin, B., StrataGen Engineering

Copyright 2009, Society of Petroleum Engineers This paper was prepared for presentation at the 2009 SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, USA, 4–7 October 2009. 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 This paper presents production evaluation criteria that can be used to compare the overall stimulation effectiveness in unconventional gas reservoirs. Characterizing the “relative” conductivity of the fracture network and primary fracture are critical to evaluating stimulation performance. Due to the uncertainty in matrix permeability and network fracture spacing (i.e. – complexity), it is difficult to find unique solutions when modeling production data in unconventional gas reservoirs. However, it may be sufficient to identify qualitative behaviors that can distinguish between key production mechanisms. This paper utilizes numerical reservoir simulation combined with advanced decline curve analyses to identify expected production signatures that can be used to evaluate stimulation effectiveness in unconventional gas reservoirs. The paper documents production signatures for low conductivity and high conductivity networks and primary fractures and discusses non-uniqueness issues with production data analysis in unconventional reservoirs. There are issues with non-unique production signatures and similar signatures may result from different combinations of matrix permeability, network block size and conductivity, and network size. However, the production signatures for high and low conductivity networks and primary fractures may be distinguished using advanced type curve graphing techniques. In addition, insights into total network size and block spacing may be possible.

The paper concludes with the evaluation of production data from 41 Barnett shale fracture treatments, including fracture and re-fracture treatments. The dataset included vertical and horizontal wells stimulated using water-fracs and cross-linked (XL) gel treatment designs. Four treatments included microseismic mapping to further constrain the interpretations. More than 60% of the XL gel treatments in vertical wells exhibited production signatures that could be interpreted as either (1) a high conductivity primary fracture connected to a low conductivity network or (2) a uniformly high conductivity network. The remaining XL gel treatments in vertical wells exhibited mostly moderate conductivity fracture network signatures. Most of the vertical wells re-fractured using slick-water treatments (water-fracs) exhibited low conductivity fracture network production signatures, with over 60% of the wells evaluated showing this signature and the remaining wells exhibiting moderate conductivity fracture network signatures. Although the initial XL gel treatments showed production signatures indicating higher fracture conductivity than the subsequent water-frac re-fracs, all of the water-frac re-fracs significantly improved gas production. Microseismic mapping has shown that water-frac re-stimulation treatments create larger and more complex fracture networks in the Barnett shale, contacting much more reservoir surface area, which explains the superior production despite the lower fracture conductivity. The majority of the horizontal wells, over 70%, showed moderate conductivity production signatures with the remaining 30% showing low conductivity signatures. The water-frac treatments in the horizontal wells appear to provide better relative fracture conductivity on average than the vertical well water-fracs. The improved network fracture conductivity could be due to design changes in the horizontal well treatments, smaller network block sizes resulting from the proximity of perforated intervals along the lateral, and/or a smaller network size being drained by each primary fracture along the lateral.

The ability to differentiate, even qualitatively, between various stimulation and completion approaches can result in much more reliable economic decisions and identification of deficiencies in current stimulation designs. The work to date indicates that improvements in network fracture conductivity and/or the conductivity of the primary hydraulic fracture could significantly improve well performance and gas recovery. This work will aid in identifying stimulation and completion techniques that create high relative conductivity fractures and/or fracture networks. These evaluations can be coupled with economic analyses to determine the most cost-effective method to stimulate production and increase gas recovery in unconventional gas reservoirs.

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Preface The paper is divided into four sections. The Introduction provides important background information on stimulation

issues in unconventional gas reservoirs. The Reservoir Simulation section documents the modeling work that formed the basis for the interpretation of production signatures in unconventional gas reservoirs; however, this section is somewhat long and the reader can find a summary of the reservoir simulation work on page 10. The Applications section presents examples of production signatures from Barnett shale wells and serves as a good overview of the proposed technique. The work is summarized and conclusions presented at the end of the paper. Due to the length of the paper and the detail presented in the reservoir simulation section, some readers may want to focus on the introduction, examples, and summary sections.

Introduction

The exploitation of unconventional gas reservoirs has become an ever increasing component of North American gas supply. The economic viability of unconventional gas developments hinges on effective stimulation of extremely low permeability rock by creating very complex fracture networks that connect huge reservoir surface area to the wellbore. The widespread application of microseismic mapping has significantly improved our understanding of fracture growth in unconventional gas reservoirs (primarily shales) and lead to better stimulation designs. However, the overall effectiveness of stimulation treatments is difficult to determine from microseismic mapping, as the location of proppant and distribution of conductivity in the fracture network cannot be measured (and are critical parameters that control well performance).

Hydraulic fracture growth in many unconventional reservoirs is very complex and unpredictable. Figure 1 shows a typical microseismic (MS) event pattern for a Barnett shale cross-linked (XL) gel frac and a water-frac re-frac, illustrating the complex fracture networks that are typical when pumping water-frac treatments in many unconventional gas reservoirs [Warpinski et. al., SPE 95568]. The dots or microseismic events show the spatial locations where the rock has been “broken” or fractured. The water-frac fracture network in Figure 1 is extremely large, covering about 140 acres; connecting millions of square feet of reservoir surface area to the wellbore and is much bigger than the XL gel treatment. Without this “picture” of the fracture, we may never have fully understood the extreme complexity of fracture growth in the Barnett shale – instead constraining our view to the planar world reinforced by our current fracture models. In the

Barnett shale, the larger and more complex the MS event patterns generally result in better the production [Mayerhofer et. al. SPE 119890]. In this example, the stimulated reservoir volume or SRV is 1450 million cubic feet of reservoir rock for the water-frac and only 430 million cubic feet for the XL gel treatment. Production results and treatment details will be discussed later, but the gas production after the water-frac was over twice that of the XL gel treatment due to the significantly larger water-frac SRV.

Figure 1. Comparison of XL Gel frac and Water-Frac Re-frac, horizontal Barnett well

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The flow capacity or conductivity of the fracture network and location of the proppant are also critical parameters that affect gas recovery. An essential aspect of modeling well performance in unconventional gas reservoirs is characterizing the flow capacity or conductivity of the fracture network and primary hydraulic fracture (if present). There are three key questions that need to be answered when trying to characterize the flow capacity of an induced fracture network.

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1. Where is the proppant located within the fracture network? 2. What is the conductivity of the propped fracture network? 3. What is the conductivity of the un-propped fracture network?

Unfortunately proppant transport cannot be reliably modeled when fracture growth is complex, making it very

difficult to predict the location of proppant within a fracture network. However, there are two limiting scenarios: (Case 1) the proppant is evenly distributed throughout the complex fracture system or (Case 2) the proppant is concentrated in a dominant primary fracture that is connected to an un-propped complex fracture network. Figure 2 illustrates the two proppant distribution scenarios. Recent studies have concluded that if the proppant is evenly distributed throughout a large complex fracture network (Case 1) then the resulting concentration of proppant is often insufficient to materially

impact network fracture conductivity. In other words, there just isn’t enough proppant pumped to effectively prop extremely large fracture networks and the fractures would behave as if they were un-propped.

If the proppant is confined within a dominant primary fracture (Case 2) the average proppant concentration would be significantly greater. This should result in much higher conductivity in the primary fracture and a better connection between the fracture network and the wellbore, which could significantly improve productivity. However, there would

be no proppant in the fracture network. In either case, well productivity in unconventional gas reservoirs may be dominated by the conductivity of un-propped or partially propped network fractures. Therefore it will be very important to understand the conductivity of un-propped and partially propped network fractures.

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Evenly Distributed

Concentrated in a dominant fracture

2

(Case 1)

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Figure 2 - Proppant transport scenarios

This paper builds upon and extends the work presented in SPE 115769, 119366, and 119368 (Cipolla et. al. 2008, 2009, 2009). One key conclusion from this prior work was that the presence of a high “relative” conductivity primary fracture can dramatically improve well performance in unconventional gas reservoirs (with complex fracture networks). SPE 119368 compared the production profiles from two Barnett horizontal wells with large fracture networks to the reservoir simulation results; the two wells followed production profiles consistent with a low conductivity fracture network – showing no sign of a high “relative” conductivity primary fracture.

Reservoir Simulation Results In this work, a series of reservoir simulations combined with advanced decline curve analyses were performed to identify expected production signatures that can be used to evaluate stimulation effectiveness in unconventional gas reservoirs. Both vertical and horizontal wells were modeled and production was simulated for 20 years for each case. In the context of horizontal wells in unconventional gas reservoirs, the study assumes that a primary fracture is propagated from each set of perforations in a cased and cemented completion. Therefore, the spacing of the primary fracture can be controlled by the perforation strategy. Additional information on modeling and production signatures can be found in SPE 108110

[Medeirios et al. 2007]; this work presents log-log production signatures for horizontal wells in a naturally fractured reservoir with multiple hydraulic fractures using a semi-analytical model. Table 1 shows the main reservoir parameters used for this

0.6Gas gravity, dimen-less

0.019Gas viscosity, cp

3.e-6Rock compressibility, 1/psi

180Reservoir temperature, °F

0.3Water saturation, fraction

3000Initial pore pressure, psi

0.03Porosity, fraction

300Net pay thickness, ft

1.e-5 and 1.e-4Reservoir permeability, md

7000Formation depth, ft











Table 1 - Main Reservoir Parameters





















Table 1 - Main Reservoir Parameters

study. The simulations covered a wide range of reservoir and

fracture characteristics, examining the spacing and conductivity of both primary and network fractures. Over 100 reservoir simulation cases were modeled, varying network fracture spacing from 50 ft to 200 ft, matrix permeability from 1e-5 md to 1e-4 md, network width from 300 ft wide to 1000 ft wide (vertical wells), primary fracture spacing from 200 to 400 ft (horizontal wells), and primary and network fracture conductivity from 0.5 to 500 md-ft. The reservoir simulation results were input into rate transient analysis software to generate log-log and Blasingame type curves. The details of the type curve development are beyond the scope of this paper, but

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the reader can refer to Appendix A for a summary of the basic equations to calculate the graphing variables. Information on type curve analyses can be found in the early work of Fetkovick 1980 and Cinco-Ley 1981 and more recent work of Blasingame 1991 and Agarwal 1999. Additional information on the development and application of type curve analyses is provided by England 2000, Pratikno 2003, Mattar 2003, Rushing 2005, Ehlig-Economides 2006, Anderson 2006, and Crafton 2006. The production profiles for the various reservoir simulations were evaluated to identify production signatures that could be used to characterize the “relative” conductivity of the primary fracture and of the network. Due to the large number of reservoir simulations and corresponding production graphs that were generated, only a selected sample of the results are presented to document the general production signatures that were identified.

Vertical Well Case: Small Fracture Network The production from a simple planar fracture and a small network with various fracture conductivities will be presented in this section. The reservoir geometry is 4,000 ft x 2,000 ft with a 1000 ft fracture half-length. For the small fracture network case, the network width is 300 ft (xn) and fracture spacing is 50 ft (Δxs or Dx) (Figure 3). Note that

the bottomhole flowing pressure is 500 psi. The log-log and Blasingame graphs of the simulated production for a single high conductivity hydraulic fracture, uniform 5 md-ft conductivity small fracture network, and the same network contacted by an infinite conductivity primary fracture are presented in Figures 4 and 5, respectively. The various

curves are labeled on these two graphs for reference, but not on subsequent graphs. The actual time that corresponds to the effective time (te) is shown on the Blasingame graphs for each log cycle (bottom portion of Figure 5). Note that the actual time differs significantly for the various simulations. The figures illustrate the difference between the production signatures for a single fracture and network fractures, as the high conductivity single fracture exhibits the classic ½-slope behavior, while a high conductivity fracture connected to a fracture network exhibits a much steeper slope (~ 1/1 or unit slope). The production signature for a uniform “moderate” conductivity fracture network exhibits a much flatter early time production signature, especially on the Blasingame derivative. Note that due to the different graphing methods the trends are positive on the log-log graph and negative on the Blasingame graph. The approximate unit slope exhibited in the network cases, which is typically an indication of drainage boundaries, is due to the flow convergence reaching the network boundaries and network blocks. The very low permeability in the rock surrounding the fracture network and the small

Figure 4. Log-log plot for single frac, small network with a uniform conductivity and infinite conductivity primary fracture, k = 1.e-4 md, kfwf = 5 md-ft (single frac or network), and Dx = 50 ft

= log-log derivative o = rate

Figure 4. Log-log plot for single frac, small network with a uniform conductivity and infinite conductivity primary fracture, k = 1.e-4 md, kfwf = 5 md-ft (single frac or network), and Dx = 50 ft

= log-log derivative o = rate= log-log derivative o = rate

Figure 3 – Small fracture network modelFigure 3 – Small fracture network model

Figure 5. Blasingame plot for single frac, uniform conductivity and infinite conductivity primary fracture small network k = 1.e-4 md, kfwf = 5 md-ft (single frac or network), and Dx = 50 ft

+ = rate integral

o = rate

= rate integral derivative

Figure 5. Blasingame plot for single frac, uniform conductivity and infinite conductivity primary fracture small network k = 1.e-4 md, kfwf = 5 md-ft (single frac or network), and Dx = 50 ft

+ = rate integral

o = rate

= rate integral derivative

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network block size give the appearance of boundary dominated flow. In the timeframe of the simulations the pressure transient does not reach the boundaries of the reservoir model, but the small matrix blocks reach pseudo steady-state flow. Similar observations have been reported by Medeiros et al. 2007.

og-log and Blasingame graphs rev

The production signatures in Figures 4 and 5 indicate that it may be possible to distinguish between production from high conductivity planar fractures and complex fracture networks, as high conductivity planar fractures exhibit ½-slope behavior, while fracture networks show much different behavior.

The impact of network fracture conductivity, in the absence of a highly conductive primary fracture, is illustrated in Figures 6 and 7. As network fracture conductivity decreases, the early time production signatures exhibit increasingly shallower slopes and different separation between the various curves. The separation between the rate (○) and derivative (▼) curves increases on both graphs as network fracture conductivity decreases, while the separation between the rate (○) and rate integral curves (+) decreases on the Blasingame

graphs. In addition, for a given fracture network size, the onset of apparent boundary effects due to flow convergence reaching the network boundary is shifted to the right (delayed in time).

Figure 6. Log-log plot for uniform conductivity small network k = 1.e-4 md and Dx = 50 ft Figure 6. Log-log plot for uniform conductivity small network k = 1.e-4 md and Dx = 50 ft

When network fracture conductivity is “relatively” high (Figures 6 and 7; 500 md-ft conductivity) the production signature is very similar to that of a high conductivity primary fracture connected to a moderate conductivity fracture network (Figure 4 and 5, infinite cond. Primary Fracture). Therefore, it may not be possible to distinguish between a high conductivity fracture network and a high conductivity primary fracture. However, this is probably not a significant limitation, as both cases indicate a highly effective stimulation – which is the objective of the fracture treatment. The Blasingame graph shown in Figure 8 further illustrates the similar production signatures when a high conductivity primary fracture is connected to a lower conductivity fracture network for varying network fracture conductivities. When network fracture conductivity is 0.5 md-ft there is a small difference in the very early time production signatures, indicating that extremely low network conductivity may exhibit an identifiable trend.

Reviewing the l

Figure 7. Blasingame plot for uniform conductivity small network k = 1.e-4 md and Dx = 50 ftFigure 7. Blasingame plot for uniform conductivity small network k = 1.e-4 md and Dx = 50 ft

eals that the Blasingame graphs are probably more diagnostic than the log-log graphs. Therefore, only the Blasingame graphs will be presented in the remainder of the paper.

Vertical Well Case: Large Fracture Network The reservoir model used to evaluate production signatures for a large network case is shown in Figure 9 (quarter reservoir symmetry), consisting of a 1000 ft network width and a fracture half-length of 1000 ft. The production signatures for network fracture conductivity ranging from 0.5 to 500 md-ft are illustrated in Figure 10. As the network size increases, the fracture conductivity required to efficiently drain the network area increases [Cipolla 2008]. Therefore, a given network fracture conductivity may appear as a high conductivity production signature when the network is small and a low or moderate conductivity signature when the network is large. When the network is large, low and high

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network fracture conductivity exhibit similar characteristics as were seen in the small network cases, but with some interesting differences that may be used to approximate network size. Large networks with low conductivity exhibit relatively flat or upward trending derivative behavior on the Blasingame graphs. As network fracture conductivity

decreases the separation between the rate and rate integral decreases and the time required for flow convergence at the network boundaries increase. The size of the network and relative network conductivity can be qualitatively diagnosed by comparing the intersection or cross-over point of the Blasingame rate and derivative curves: higher conductivity and smaller networks exhibit earlier intersections.

Figure 11 shows the production signatures for a matrix permeability of 1e-5 md, ten times lower than the previous

cases. Comparing Figure 10 and Figure 11 shows some fferences in the early time behavior of the Blasingame

vative, but the general signatures for high and low acture conductivity is similar. Figure 12 shows the oduction signature for an infinite conductivity primary

diderifrprfracture in contact with a large fracture network; matrix permeability is again 1e-5 md. The network fracture conductivity is varied from 0.5 md-ft to 500 md-ft. As the network conductivity decreases to 0.5 md-ft, the production signature deviates somewhat from that of a high conductivity primary fracture, which could lead to some ambiguous production signatures when network fracture conductivity is extremely low (relative to the matrix permeability, block size, and network size).

Effect of network block size or spacing The impact of network block size on the production

signature is shown in Figure 13 and Figure 14 for a block size of 200 ft and matrix permeability of 1e-4 md and 1e-5 md, respectively. The network fracture conductivity is varied from 0.5 md-ft to 500 md-ft and is uniform

Figure 10. Blasingame plot for uniform conductivity large network, k = 1.e-4 md and Dx = 50 ftFigure 10. Blasingame plot for uniform conductivity large network, k = 1.e-4 md and Dx = 50 ft

Figure 8. Blasingame plot for infinite conductivity primary fracture and small network, k = 1.e-4 mdand Dx = 50 ft

Figure 8. Blasingame plot for infinite conductivity primary fracture and small network, k = 1.e-4 mdand Dx = 50 ft

Figure 9. Large fracture network modelFigure 9. Large fracture network model

Figure 11. Blasingame plot for uniform conductivity large network k = 1.e-5 md and Dx = 50 ftFigure 11. Blasingame plot for uniform conductivity large network k = 1.e-5 md and Dx = 50 ft

SPE 124843 7

throughout the network. The basic signatures for low anhigh conductivity fracture networks are similar to thprevious cases with 50 ft block size (Figures 10 and 1However, as the network block size increases the tirequired for the blocks to reach pseudo flow also increaseFor the same matrix permeability and network fracture

d e

1). me

s.

es the cross-

in is

c ity ub

c tivity h ear eea e c ity

r

etwork block size and conductivity, and network size. However, the production sig

orizontal well completed wit

of the area is simulated assuming reservoir symmetry. A dry gas reservoir without water production is modeled. The

conductivity, as network block size increasover point or intersection of the Blasingamand rate-integral derivative curve occurs mtime. The extended transient flow time evident when network fracture conductivityand 5 md-ft).

The production signatures for an infinite primary fracture connected to a large fractare shown in Figure 15 and Figure 16 for a 200 ft and matrix permeability of 1e-4 anrespectively. The signature of a high fracture is similar to previous cases, except tflow (1/2-slope) period is more pronouncnetwork block size is larger and whconductivity is low. This may provide method to distinguish between high networks and high conductivity prima

e rate curve uch later especially is low (0.5

onductivre network lock size ofd 1e-5 md, onducat the lind when the n network qualitativonductiv

y fractures connected to low conductivity network when network block size is large and matrix permeability is low.

There clearly are issues with non-unique production signatures and similar signatures may result from different combinations of matrix permeability, n

natures for high and low conductivity networks and primary fracture may be distinguished using Blasingame and log-log graphs. In addition, insights into total network size and block spacing may be possible. The following section will examine the production signatures for horizontal wells.

Production Signatures for Horizontal Wells The following results show the production signatures (i.e. Blasingame plots) for the case of a h

h multiple hydraulic fracture treatments. The well, reservoir, and fracture parameters are given in SPE 119366 [Cipolla et. al, (2009)]. The reservoir properties are the same as those shown in Table 1. Figure 17 shows the pressure distribution when Stimulated Reservoir Volume (SRV) is 2,000x106 ft3. The reservoir size is 3400 ft x 2400 ft. One-half

Figure 12. Blasingame plot for infinite conductivityprimary fracture, large network, k = 1.e-5 md and Dx = 50 ft

Figure 12. Blasingame plot for infinite conductivityprimary fracture, large network, k = 1.e-5 md and Dx = 50 ft

Figure 13. Blasingame plot for uniform

Figure 14. Blasingame plot for uniform colarge network, k = 1.e-5 md and Dx = 200

nductivity ft

Figure 14. Blasingame plot for uniform colarge network, k = 1.e-5 md and Dx = 200

clarge network, k = 1.e-4 md and Dx = 200

onductivity ft

Figure 13. Blasingame plot for uniform clarge network, k = 1.e-4 md and Dx = 200

onductivity ft

nductivity ft

8 SPE 124843

network consists of main (primary) and secondary fractures (fracture network). The main fracture, representing the propped frac, is connected to the wellbore and is oriented perpendicular (transverse) to the direction

ractures or

and the network fracture co

uctivity is low, approaching that of the

(less than 1/4-slope), while the rate-int

of the horizontal wellbore. The secondary ffracture network, assumed to be un-propped in this study, have no direct communication with the wellbore. The

secondary fractures are placed in the perpendicular and parallel directions to the main fracture. Gas flow occurs into the wellbore through the main fractures only. In Figure 17, the main fractures are evenly spaced (400 ft) and extend 1,000 ft (half-length) from the wellbore. The secondary or network fractures are evenly spaced at 100 ft in both directions. Note that the distance between the first main frac and the last main frac is 2,800 ft.

Figure 17 shows the effect of a high conductivity primary fracture that is connected to a large, low conductivity fracture network. When network fracture conductivity is low, drainage is much more efficient when a high conductivity primary fracture is present. This is shown by the significantly lower pressures throughout the fracture network (light and dark blue colors) in the top graphic in Figure 17 compared to the much high pressures in lower graphic in Figure 17.

Impact of Primary Fracture Conductivity, 0.5 md-ft Network Fracture Conductivity, 400 ft Primary Frac Spacing

The impact of primary fracture conductivity on the production signatures for horizontal wells is examined in the following sections. In this section the primary fracture spacing is 400 ft

Figure 15. Blasingame plot for infinite conductivity primary fracture, large network, k = 1.e-4 md and Dx = 200 ft




2 mD-ft uniform network conductivity

Pressure (psi)

Pressure (psi)

100 mD-ft primary frac conductivity 400 ft400 ft

Figure 17. Horizontal well, 400 ft main frac spacing, 100 ft network blocks, 2 mD-ft network conductivity. Pressure distribution after 1 yr, with (top) and without (bottom) a high conductivity primary frac.

Matrix k = 0.0001 md


2 mD-ft uniform network conductivity

Pressure (psi)

Pressure (psi)

100 mD-ft primary frac conductivity 400 ft400 ft

Figure 17. Horizontal well, 400 ft main frac spacing, 100 ft network blocks, 2 mD-ft network conductivity. Pressure distribution after 1 yr, with (top) and without (bottom) a high conductivity primary frac.



nductivity is 0.5 md-ft. Figure 18 shows the Blasingame graph for the horizontal well case when permeability is 1e-5 md, while Figure 19 shows the Blasingame graph for the horizontal well case when permeability equals 1e-4 md. In each graph, the primary or propped fracture conductivity is varied 0.5, 5, 50, and 500 md-ft. The production signatures for low and high conductivity primary fractures are similar to the vertical well case. When the primary fracture condnetwork fractures, the Blasingame rate and rate-integral curves show very little separation and a shallow slope

egral derivative exhibits a large separation from the rate and rate-integral curves and a prolonged period of

SPE 124843 9

near-flat to upward trending behavior. The impact of matrix permeability is also similar to the vertical well cases, with lower matrix permeability exhibiting longer transient linear flow (1/2-slope) signatures. A similar series of reservoir simulations were performed assuming

a network fracture conductivity of 5 md-ft and showed essentially the same production signatures when primary fracture conductivity was varied from 5 md-ft to 500 md-ft.

Impact of Primary Fracture Conductivity Assuming 0.5 md-ft Secondary or Network Fracture Conductivity and 400 ft Primary Fracture Spacing

In this section the primary fracture spacing is 200 ft and the network fracture conductivity is 0.5 md-ft. Figure 20 shows the Blasingame graph for a horizontal well with matrix permeability of 1e-5 md, while Figure 21 shows the Blasingame graph for the horizontal well case with matrix permeability of 1e-4 md. As with the previous cases, the primary or propped fracture conductivity is varied 0.5, 5, 50, and 500 md-ft. Comparing Figures 20 and 21 with Fi

Figure 18. Blasingame plot for horizontal well case with 1.e-5 md matrix permeability, 400 ft primary fracture spacing, and 0.5 md-ft network fracture conductivity


gures 18 and 19 shows essentially the same production signatures for 200 ft and 400 ft primary

ctivity primary fractures may not be materially

ature.

fracture spacing (for the same matrix permeability). Therefore, the ability to distinguish between low and high conduimpacted by completion practices (i.e. – the number of stages or distance between perforated intervals). In addition, low and high conductivity primary fractures may be identified for matrix permeability ranging from 1e-4 to 1e-5 md, as the production signatures are essentially the same within this range of matrix permeability.

Impact of Effective Propped Fracture Length The previous simulations assumed that the primary or

propped fracture extended to the boundary of the fracture network (xf = 1000 ft). However, the effective or propped length of the primary fracture may not extend to the network boundary. The impact of effective or propped length of the primary fracture on well productivity is discussed in SPE 119366 [Cipolla 2009]. This section illustrates the impact of effective primary fracture length on the Blasingame production sign



Figure 20. Blasingame plot for horizontal with 1.e-5 md matrix permeability, 200 ft fracture spacing, and 0.5 md-ft network frconductivity

wpa

ell case rimary cture

Figure 20. Blasingame plot for horizontal wwith 1.e-5 md matrix permeability, 200 ft pfracture spacing, and 0.5 md-ft network fraconductivity

ell case rimary cture

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Figure 22 shows the production signatures for eff d 1000 ft i

fective length of the

of 0.5 and 5 md-ft. The production

ical production signatures for low, medium, roduction signatures vary depending on the network es attempt to approximate the general production

ective primary fracture lengths of 0, 250, 500, ann a horizontal well with 400 ft primary fracture spacing,

0.5 md-ft network fracture conductivity, and 1e-5 md matrix permeability. The primary fracture conductivity is 500 md-ft, resulting in an infinite conductivity primary fracture. The typical low conductivity production signature is shown when there is no high conductivity primary fracture (0 ft case), which corresponds to the bottom (green) set of curves in Figure 18 for reference. Conversely, when the ef

primary fracture extends to the network boundary (1000 ft), a typical high conductivity production signature is exhibited (which is the same as the top black set of curves in Figure 18).

As the effective length of the high conductivity primary fracture is decreased from 1000 ft to 250 ft, there is little

change in the production signature with the exception of the cross-over point or intersection of the Blasingame rate and rate-integral derivative curves. As the effective length of the primary fracture decreases the time required to reach the intersection point increases. A similar set of reservoir simulations were run for a matrix permeability of 1e-5 md and a network fracture conductivity of 5 md-ft and a matrix permeability of 1e-4 md with network fracture conductivity



signatures for these cases also showed similar behavior as the effective length of the primary fracture decreased. Therefore, diagnosing high and low conductivity primary fractures connected to low conductivity networks should not be materially affected by uncertainties in the effective length of the primary fracture. However, it may not be possible to determine the effective length of the primary fracture using the production signature due to uncertainties in network block size, matrix permeability, and network fracture conductivity.

Summary of Reservoir Simulations and Production Signatures

There are a number of reservoir and fracture parameters that affect the production signatures for unconventional gas wells and many of these parameters are not well understood and/or difficult to measure. However, the reservoir simulation work suggests that it may be possible to distinguish between high and low conductivity fracture networks using the Blasingame and log-log production graphs. Figure 23 illustrates typ

Figure 22. Blasingame plot for horizontal well caseshowin

g the impact of effective propped fracture

length for 1.e-5 md matrix

permeability, 400 ftprimary fracture spacing, 500 md-ft

primary fracture

conductivity, and 0.5 md-ft network fractureconductivity

Figure 22. Blasingame plot for horizontal well caseshowin

g the impact of effective propped fracture

length for 1.e-5 md matrix

permeability, 400 ftprimary fracture spacing, 500 md-ft

primary fracture

conductivity, and 0.5 md-ft network fractureconductivity

and high conductivity networks. Although the transient early time pblock size, total network size, and matrix permeability, the curvsignatures as a function of network fracture conductivity. Highly conductive networks will exhibit much steeper early time trends, more separation between the rate and rate-integral curves, and less separation between the rate-integral and rate-integral derivative. In addition, highly conductive fracture networks will typically show apparent boundary dominated flow (unit slope on the rate curves) much earlier in time compared to low conductivity networks.

Figure 24 compares the production signatures for an infinite conductivity primary fracture in a low conductivity fracture network (0.5 md-ft) with 50 ft network blocks to a high uniform conductivity network (50 md-ft) with 200 ft network blocks, k=1e-5 md for both cases. Both cases exhibit production signatures that indicate good fracture conductivity. When network block size is 50 ft the transition to an apparent boundary dominated flow signature (unit

SPE 124843 11

slope) occurs much earlier in time compared to a 200 ft block size. The time to reach an apparent boundary dominated flow signature is a function of the network block size, total network size, primary fracture conductivity, net

at result in high conductivity fractures (primary or

f the mechanisms for these improvements has primarily been provided by microseismic monitoring of t

at result in high conductivity fractures (primary or

f the mechanisms for these improvements has primarily been provided by microseismic monitoring of t

work fracture conductivity, and matrix permeability. As many of these parameters are not well known, it may not be possible to easily distinguish between the signatures from a high conductivity primary fracture connected to a low conductivity network from that of a uniformly high conductivity network. However, it should be possible to distinguish between stimulation treatments that create complex networks with low and high conductivity.

Production Signatures for Barnett Shale Wells Although the Blasingame production signatures cannot provide quantitative results, comparing the signatures from various stimulation designs and completion strategies may help to more reliably identify the practices that work best. Due to variations in rock quality, thickness, pre-existing rock fabric or natural fractures, etc., simple well to well production comparisons may not reliably indicate the best stimulation

design or completion practice. Utilizing the production signatures can help identify practices th

from that of a uniformly high conductivity network. However, it should be possible to distinguish between stimulation treatments that create complex networks with low and high conductivity.

Production Signatures for Barnett Shale Wells Although the Blasingame production signatures cannot provide quantitative results, comparing the signatures from various stimulation designs and completion strategies may help to more reliably identify the practices that work best. Due to variations in rock quality, thickness, pre-existing rock fabric or natural fractures, etc., simple well to well production comparisons may not reliably indicate the best stimulation

design or completion practice. Utilizing the production signatures can help identify practices th

High conductivity network

Low conductivity network

Moderate conductivity network

Rate

Rate Integral

Derivative of Rate Integral

Figure 23. General production signatures for low, medium, and high conductivity fracture networks

High conductivity networkHigh conductivity network

Low conductivity networkLow conductivity network

Moderate conductivity networkModerate conductivity network

Rate

Rate Integral

Derivative of Rate Integral

Figure 23. General production signatures for low, medium, and high conductivity fracture networks

network). Combining microseismic mapping, network). Combining microseismic mapping, reservoir simulation, and matrix permeability measurements with production signatures may lead to better understanding of the network block size, conductivity of the network and primary fractures, and the “effective” network size. It is beyond the scope of this paper to present case histories that document this integrated application of production signatures, as this technique is still in the development and evaluation stage. However, examples are presented in this section of the paper that illustrates the production signatures for actual Barnett shale completions.

The stunning success of gas development in the Barnett shale of North Central Texas is largely due to the optimization of hydraulic fracturing in this unusual reservoir environment and horizontal well development. In particular, the migration from cross-linked gel, propped fracture treatments to water-fracs

with higher rates and lighter proppant loadings has provided both technical and economic benefits by improving connectivity within the reservoir while reducing costs. While these benefits have been seen directly in gas production, a full understanding o

reservoir simulation, and matrix permeability measurements with production signatures may lead to better understanding of the network block size, conductivity of the network and primary fractures, and the “effective” network size. It is beyond the scope of this paper to present case histories that document this integrated application of production signatures, as this technique is still in the development and evaluation stage. However, examples are presented in this section of the paper that illustrates the production signatures for actual Barnett shale completions.

The stunning success of gas development in the Barnett shale of North Central Texas is largely due to the optimization of hydraulic fracturing in this unusual reservoir environment and horizontal well development. In particular, the migration from cross-linked gel, propped fracture treatments to water-fracs

with higher rates and lighter proppant loadings has provided both technical and economic benefits by improving connectivity within the reservoir while reducing costs. While these benefits have been seen directly in gas production, a full understanding o

Figure 24. Blasingame plot for 0.5 md-ft large network with infinite conductivity primary fracture, Dx = 50 ft and 50 md-ft uniform conductivity large network, Dx = 200 ft, k = 1.e-5 md

½-slope

1/1-slope



½-slope½-slope

1/1-slope1/1-slope

he treatments. It is the ability to observe the development of microseismic patterns that has given considerable insight into the mechanism for the success of water-fracs. The Barnett shale is a complicated, naturally-fractured reservoir with matrix permeability less than 0.0001 mD; however, it is believed that the natural fractures are almost exclusively mineralized and do not contribute to well productivity unless they are stimulated (activated). Large-volume water-fracs pumped at high rates have been shown to effectively stimulate substantial volumes of the reservoir through the

he treatments. It is the ability to observe the development of microseismic patterns that has given considerable insight into the mechanism for the success of water-fracs. The Barnett shale is a complicated, naturally-fractured reservoir with matrix permeability less than 0.0001 mD; however, it is believed that the natural fractures are almost exclusively mineralized and do not contribute to well productivity unless they are stimulated (activated). Large-volume water-fracs pumped at high rates have been shown to effectively stimulate substantial volumes of the reservoir through the

12 SPE 124843

development of an interconnected fracture system in two orthogonal directions which is the key to maximizing gas production in such a low permeability reservoir.

Production data for 28 wells in the core area of the Barnett shale were gathered from a commercial database. The wells consisted of 12 older vertical wells completed between 1991 and 1994, 1 newer vertical well completed in 2001, and 15 horizontal wells completed in 2003-2005. Based on the timing of the completions in the vertical wells, it was assumed that cross-linked (XL) gels were used for the older vertical well completions [Frantz 2005]. Historically, most of the

to be 500 psi for the vertical wells and 1,000 psi for the horizontal wells. Only a sel

ll Example 1 Figure 25 compares the production history for

frac re-frac. The water-frac significantly ou

a are not available for these treatments, it has been do create more complex

fr

older vertical Barnett wells were re-fractured using water-frac stimulations. This evaluation compared the production signatures for the initial XL gel frac and the water-frac re-frac. The actual fracture treatment data were not available for 24 of the 28 wells, but the intent of the evaluation was simply to examine the production signatures for vertical and horizontal completions and XL gel and water-frac stimulation designs. The dataset included four wells with microseismic mapping and detailed fracture treatment information, three horizontal wells and one vertical well. In total, production signatures for 41 fracture treatments were evaluated, consisting of 12 XL gel treatments in vertical wells, 12 water-frac re-fracs in vertical wells, 1 vertical water-frac, 14 water-fracs in horizontal wells, 1 XL gel frac in a horizontal well, and 1 water-frac re-frac in a horizontal well.

The production data were evaluated using log-log and Blasingame graphs to identify and compare production signatures of XL gel jobs, water fracs, re-fracs for vertical and horizontal wells. Total net pay is 300 ft and porosity is 3%. The reservoir temperature is 210 °F, reservoir pressure 3000-3800 psi, and the water saturation is 30%. The bottomhole flowing pressure is assumed

ected sample of the results is presented to document the various production signatures that were identified. Production Signatures for Vertical Barnett Wells

The production signatures for vertical Barnett wells are examined in this section, which includes the initial XL gel fracture treatment and the water-frac re-frac, plus one newer water-frac well.

Vertical We

Figure 25. Production data for vertical well example 1

Water-frac re-frac

Initial XL Gel frac

0

5

10

15

20

25

30

0 730 1460 2190 2920 3650

Time (Days)

Gas

Pro

duct

ion

(MM

CF/

Mo.

)


Water-frac re-frac

Initial XL Gel frac

0

5

10

15

20

25

30

0 730 1460 2190 2920 3650

Time (Days)

Gas

Pro

duct

ion

(MM

CF/

Mo.

)

the initial XL gel fracture treatment and the water-

t-performs the XL gel treatment, which is likely due to the water-frac contacting much more reservoir surface area than the XL gel frac. Although microseismic fracture mapping dat

cumented that water-fracs acture networks than XL gel treatments [Warpinski

2005, Cipolla 2008]. The Blasingame graph for the initial XL gel treatment is shown in Figure 26, exhibiting a ½-slope behavior on all three curves that is indicative of a high “relative” conductivity hydraulic fracture, but showing none of the typical production signatures for network fracture flow. At first glance this would appear to be an effective stimulation. However, in the case of many shale reservoirs that are prone to fracture complexity or network fracture growth, maximizing the fracture complexity and increasing the stimulated reservoir volume (SRV) can significantly improve well productivity and ultimate gas recovery [Mayerhofer 2008, Cipolla 2008]. Figure 27 shows the Blasingame graph for the water-frac re-frac, exhibiting a production signature of a high conductivity fracture network with either low permeability matrix or large network blocks. The production signature in Figure 27 is very similar to the bottom set of curves in Figure 24 (large fracture network, large network blocks, and low matrix permeability).

Figure 26. Blasingame plot for a XL gel treatment, vertical well example 1

½-slope

Figure 26. Blasingame plot for a XL gel treatment, vertical well example 1Figure 26. Blasingame plot for a XL gel treatment, vertical well example 1

½-slope

SPE 124843 13

It should be noted that the Blasingame graphs for the re-fracs assumed the same reservoir pressure as the initial XL gel stimulation. In general, uncertainties in reservoir pressure will shift all the curves up or down, but will not significantly affect the production signatures.

Vertical Well Example 2 Figure 28 compares the production for the initial XL

gel frac and water-frac re-frac for the second vertical well example. Again the water-frac re-frac significantly out pe orms the initial XL gel treatment. Figure 29 shows the XL gel treatment, exhibiting a p

latively fla

d reservoir modeling were presented in SPE 102103 [Mayerhofer 2006]. Figure 31 shows the microseismic apping results for the water-frac stimulation, indicating a complex fracture network that is approximately 350 ft wide

and ext of the simulations presented in this paper, the fracture network would be considered small (reference Figure 3). The microseismic (MS) data provide a measurement of the maximum extent of the fracture

rf Blasingame graph for theroduction signature of a high conductivity network or

high conductivity primary fracture that is connected to a low conductivity fracture network. This is evidenced by the separation between the rate and rate-integral curves and the decreasing middle to late-time rate-integral derivative curve (see Figure 24 for typical curve shapes). Therefore, it may be possible to create fracture networks

with XL gel treatments – but again the extent of the fracture network and probably the conductivity of the network will be limited due to the high viscosity of the XL gel. The intersection of the rate and rate-integral derivative curves combined with the transition to unit-slope in the rate curve is a qualitative indication of the network size.

Figure 30 shows the Blasingame graph for the water-frac re-frac, exhibiting a production signature of a moderate conductivity fracture network. This is shown by the close proximity of the rate and rate-integral curves and the re

Figure 27. Blasingame plot for a water-frac re-frac treatment in vertical well example 1

½-slope

Figure 27. Blasingame plot for a water-frac re-frac treatment in vertical well example 1Figure 27. Blasingame plot for a water-frac re-frac treatment in vertical well example 1

½-slope

t middle to late-time rate-integral derivative curve. The intersection or cross-over point of the rate and rate-integral derivative curves occurs at about 9 years and there is no clear transition to

unit-slope behavior on the rate curve. The intersection point for the XL gel treatment occurred around 4 years. Comparing the intersection points of XL gel and water-frac treatments would indicate that the water-frac treatment created a much larger fracture network, as it takes much longer to see the boundaries of the network for the water-frac. It appears from the production signatures that the water-frac created a larger, but lower conductivity fracture network. It seems that the increase in fracture surface area or SRV with the water-frac, even with the apparent decreased fracture conductivity, resulted in significant production enhancement compared to the XL gel treatment. However, if network and/or primary fracture conductivity can be improved in the water-frac treatments it may be possible to significantly increase production [Cipolla 2008, Cipolla 2009].

Vertical Well Example 3 Example 3 is a newer vertical well that was

stimulated using a water-frac treatment. Microseismic data an

0

5

10

15

20

25

30

35

40

0 730 1460 2190 2920 3650

Time (Days)

Gas

Pro

duct

ion

(MM

CF/

Mo.

)


Water-frac re-frac

Initial XL Gel frac

0

5

10

15

20

25

30

35

40

0 730 1460 2190 2920 3650

Time (Days)

Gas

Pro

duct

ion

(MM

CF/

Mo.

)


Water-frac re-frac

Initial XL Gel frac

Figure 29. Blasingame plot for a XL gel treatment in vertical well example 2

½-slope unit-slope

Intersection or cross-over

Figure 29. Blasingame plot for a XL gel treatment in vertical well example 2

½-slope unit-slope


m 2200 ft long. In the cont

14 SPE 124843

net

ion point in Figure 7

network, with the remaining treatments exhibiting mostly moderatecon

scosity of XL gels and the large pro

acture, which may be the case for the XL gel treatments, a high con

work, but cannot provide an estimate of the network properties such as conductivity and block size. However, integrating the MS data with reservoir simulation and/or production signature analyses may provide insights into network properties that can be used to improve future stimulation designs.

The Blasingame graph of the production data from Well 3 is shown in Figure 32, illustrating a production signature of a moderate conductivity fracture network. Comparing the production signature in Figure 32 to the reservoir simulation results in Figure 7 shows a production signature similar to the 5 md-ft network conductivity case. However, the cross-over or intersection point of the Blasingame rate and rate-integral derivative curves in Figure 32 occurs around 6-7 years (end of the production history), while the intersect

occurs after 1 year of production for the 5 md-ft case. The delay in the intersection point (Figure 32) is likely due to larger network block size created during the

Example 3 treatment. Without the MS data it would be difficult to distinguish between larger network block sizes and a larger network. The reservoir simulation history match presented in SPE 102103 also indicated relatively large network block sizes and moderate network conductivity, consistent with the qualitative interpretation using the Blasingame production signature.

Summary of Vertical Well Production

Signatures Only a sample of the vertical well

productions signatures are presented in the paper, but are representative of the behavior of the majority of the wells evaluated. Over 60% of the XL gel treatments in vertical wells exhibited production signatures of a high conductivity primary fracture connected to a low conductivity network or a uniformly high conductivity


½-slope



½-slope


½-slope


ductivity fracture network signatures. Given

the high vippant volumes typically pumped in these

treatments, the mostly likely explanation is that the primary fracture created using XL gels is highly conductive relative to the very low permeability shale. In addition, the high viscosity fluid probably limits the network size and results in low network conductivity due to damage from the gels. Poor cleanup of the primary fracture could be the reason for the cases where moderate conductivity fracture network signatures are exhibited.

The water-frac re-fracs exhibited mostly low conductivity fracture network production signatures, with over 60% of the wells evaluated showing this signature. The remaining wells exhibited moderate conductivity fracture network signatures. As was evidenced in Figure 32, water-fracs create very complex fracture networks. It appears from the production signatures that the proppant pumped during these treatments did not result in a high conductivity primary fracture. If the proppant was contained in a primary fr

Figure 31. Microseismic data for Vertical Well Example 3

~350 ft

~2200 ft

~350 ft

~2200 ft

Figure 31. Microseismic data for Vertical Well Example 3

~350 ft

~2200 ft

~350 ft

~2200 ft

ductivity primary fracture signature might be expected. However, if the proppant is more evenly distributed throughout the complex fracture network then the volumes pumped would likely be insufficient to materially affect network fracture conductivity and the network fractures would behave as un-propped fractures [Cipolla 2008]. Network fracture conductivity of 0.5 to 5 md-ft resulted in low to moderate conductivity production signatures in the simulations and this range is consistent with laboratory estimates of un-propped fracture conductivity for rock properties and stress similar to the Barnett [Fredd 2001]. Therefore, the production signatures for the water-fracs indicate that improving

SPE 124843 15

fracture conductivity could result in significant production increases, while the production for water-fracs indicate that the increase in SRV that results from the low viscosity fluid (and larger volumes and higher rates) more than offsets the loss of conductivity – significantly out-performing XL gel treatments.

It was beyond the scope of this work to investigate the details of each treatment, compare the production histories and well performance, and evaluate geological and well location information. However, integrating such data with production signature evaluations could result in significant insights into stimulation performance and assist in comparing different stimulation designs and completion strategies.

Production Signature for Horizontal Barnett Wells

In this section the production signatures for horizontal Barnett completions are examined. All of the

er-frac treatments in the horizontal wells seem to show better relative fracture conductivity on average than the vertical well

ivity could be due to design changes in the horizontal well trea

perforation clusters along the ~2000 ft lateral length and the entire lateral was stimulated in a single treatment. The original stimulation consisted of a p he longitudinally oriented lateral to assess whether improvements could be made in hor

lid blue line in Figure 33 is daily production data, while the red dot

at was interpreted from the vertical well XL gel production signatures. The lower conductivity could be due to ineffective cleanup of the XL gel and conductivity may have increased with prolonged

Figure 32. Blasingame graph Vertical Well Example 3

½-slope

Figure 32. Blasingame graph Vertical Well Example 3

½-slope

horizontal wells were stimulated using large volume, high rate water-frac treatments. Three examples are presented in this section illustrating the production signatures observed in the 15 horizontal wells that were studied. The majority of the horizontal wells, over 70%, showed moderate conductivity production signatures with the remaining 30% showing low conductivity signatures. The wat

water-fracs. The improved network fracture conducttments, smaller network block sizes resulting from the proximity of perforated intervals along the lateral, and/or a

smaller network size being drained by each primary fracture along the lateral. Horizontal Well Example 1 The first horizontal well example is unique in that it provides MS mapping and production data for a XL gel

treatment and a water-frac re-frac treatment, providing a comparison of both the SRV (i.e. – fracture network size and fracture complexity) and the well performance for low and high viscosity fluids. A series of stimulations were performed in a horizontal well over a several month period. The well was cased and cemented with six

ropped, XL gel treatment in tizontal well productivity by changing the stimulation approach. The MS mapping results for the XL gel treatment are

shown in the left frame in Figure 1. Virtually all Barnett shale horizontal wells are oriented transverse to the primary direction of hydraulic fracture growth and stimulated with multiple fracture treatment stages. The wellbore orientation and single fracture treatment is another unique aspect of this example which provides important insights into stimulation effectiveness. After several months of poor productivity, the well was re-fractured using a water-frac. The MS mapping data for the water-frac re-frac are shown in the right frame of Figure 1. Additional details of the MS mapping for this example can be found in SPE 95568 [Warpinski 2005]. Figure 1 shows that the initial XL gel treatment resulted in a relatively linear hydraulic fracture with a very limited fracture network, while the water-frac re-frac resulted in a complex fracture network. The water-frac created a Stimulated Reservoir Volume, or SRV, of approximately 1,450,000,000 ft3 compared with only 430,000,000 ft3 in the XL gel frac.

The XL gel treatment consisted of a cross-linked gel stimulation pumped at 70 bpm for about three hours, with sand concentrations ramped up to about 3 ppg. Total volumes were 11,600 bbl of 25# cross-linked gel and 700,000 lb of sand. The water-frac re-frac was pumped at 125 to 130 bpm for most of the treatment, but tapered off to near 90 bpm at the end. The total duration of the injection was about 6.5 hours and the volumes pumped were 60,000 bbl of slick-water and 385,000 lb of sand. The production data for the XL gel and water-frac re-frac are shown in Figure 33, illustrating significant production improved after the re-frac. The so

s represent monthly data. The water-frac re-frac production is over 2 times that of the XL gel. The significant improvement in production is attributed to the much larger SRV created by the water-frac re-frac, which was documented from the MS mapping data.

The Blasingame production graph for the XL gel frac is shown in Figure 34, exhibiting a production signature for a low conductivity fracture. This is evidenced by the close proximity and the approximate ¼-slope behavior of the rate and rate-integral curves and the separation between these curves and the rate-integral derivative. Contrasting this with the vertical well production signatures for XL gel treatments in the previous section shows a striking difference. Although this well was produced for only 6 months prior to the re-frac, it seems that the XL gel treatment did not create a highly conductive primary fracture th

16 SPE 124843

pr

likely explanation for the poor conductivity of the two fracture treatments is the longitudinal ori

out 2,600 ft. The water-frac treatments consisted of 830 klb of 0/70-sand and 117,000 bbl of fluid pumped in two stages with three perforation clusters per stage (6 perforation clusters

in t from 80 to 120 bpm. The microseismic fracture mapping indicated an

oduction. It is also possible that the longitudinally orientated ~2000 ft lateral could not be effectively stimulated, possibly due to the orientation of the wellbore with respect to the natural fractures and single stage treatment.

Figure 35 shows the Blasingame production graph for the water-frac re-frac. The water-frac re-frac exhibits the production signature of a low conductivity fracture network. As will be shown in subsequent examples, most of the horizontal wells exhibited production signatures indicating moderate conductivity networks. Although the SRV was dramatically increased and the production was significantly improved with the water-frac re-frac, it appears that the conductivity of the network was still very low. The production signatures for the two treatments, when compared to other wells,

provide insights into stimulation effectiveness that may not be possible from well performance comparisons alone. In this example, it appears that the

Figure 33. Production for Horizontal Well Example 1

XL Gel

Water-frac re-frac

Figure 33. Production for Horizontal Well Example 1

XL Gel

Water-frac re-frac

entation of the horizontal well combined with the single stage fracture treatment.

Horizontal Example Well A and Example Well B Horizontal Example Wells A and B are close offsets to each other and MS mapping data are available for both water-

frac treatments. The details of these two wells were presented in SPE 119366 [Cipolla 2009]. Well A has a lateral section of about 2,600 ft. The water-frac treatments consisted of four stages (one perforation cluster per stage) with each cluster 700 ft apart. The injection rate for each stage was ~70 bpm. The total amount of proppant pumped was 670 klb (40/70-sand) with 120,000 bbl of fluid. The microseismic fracture mapping showed a total SRV of 1,880x106 ft3 for the Well A treatments. Well B also has a total lateral section of ab

Figure 35. Blasingame graph for Horizontal Well Example 1 (water-frac re-frac, monthly data)

¼-slope

Figure 35. Blasingame graph for Horizontal Well Example 1 (water-frac re-frac, monthly data)Figure 35. Blasingame graph for Horizontal Well Example 1 (water-frac re-frac, monthly data)

¼-slope

Figure 34. Blasingame graph for Horizontal WellExample 1 (XL Gel frac, daily data)

¼-slope

Figure 34. Blasingame graph for Horizontal WellExample 1 (XL Gel frac, daily data)

¼-slope

4otal, spaced 500 ft apart). The injection rate ranged SRV of 2,017x106 ft3 for Well B. The cumulative gas production for Wells A and B are shown in Figure 36 (from

SPE 119366) and are compared to reservoir simulations for a horizontal completion with a moderate conductivity fracture network (2 md-ft, SRV = 2,000x106 ft3). Previous work showed that the production profile for Wells A and B were consistent with a low to moderate conductivity fracture network [Cipolla 2009].

Figure 37 shows the Blasingame graph for Well A, exhibiting a production signature for a moderate conductivity fracture network. Figure 38 shows the Blasingame graph for Well B, exhibiting a production signature for a low conductivity fracture network. The cross-over point or intersection of the rate and rate-integral derivative curves in an indication of either a smaller total network size or smaller average block size. In this case, the network size is known

SPE 124843 17

from the MS mapping and is about the same for both wells. Therefore, the earlier intersection time for the rate and rate-integral derivative curves in Well A compared to Well B is probably an indication of smaller network block size in Well A. This is consistent with the reservoir simulation results shown in Fi

modeled, limiting some analytical dual porosity solutions that assume pseudo steady-state flow in the

rk did not include the effect of

lations and evaluation of Barnett shale production

gure 36. Based on the production signatures for Wells A and B, it appears that the stimulation in Well A is more effective than Well B – illustrating the potential application of production signatures to quickly compare stimulations and completions. It is possible that the 4-stage stimulation in Well A was more effective than the 2-stages in Well B, resulting in more fracture complexity. Summary and Conclusions In this work numerical reservoir modeling was used to predict the log-log and Blasingame production signatures for unconventional gas wells. Although analytical methods would

likely be a faster and more elegant solution, they are not readily available in commercial production analysis software. Hopefully analytical models will soon be added to commercial production analysis software that can adequately represent shale-gas production behavior. In addition, the low permeability of the reservoir matrix rock often requires that the transient flow be properly

Well A

Well B

Figure 36. Production for Wells A & B and simulated production for different network fracture spacing

Well A

Well B

Well A

Well B

Figure 36. Production for Wells A & B and simulated production for different network fracture spacing

matrix blocks. This wogas desorption on production behavior, which may not be that important in the Barnett shale, but can easily be included using numerical reservoir modeling when required. The reservoir simulations covered a wide range of reservoir, completion, and fracture parameters that are typical of many unconventional shale-gas reservoirs, with a focus on properties of the Barnett shale. Both vertical and horizontal completions were simulated representing small and large networks with varying degrees of fracture complexity and matrix permeability ranging from 1e-4 to 1e-5 md. The log-log and Blasingame production graphs from the reservoir simulations were evaluated and many common signatures were identified. The production signatures for Barnett shale wells were also evaluated and compared to the basic signatures identified from the simulation work.

The goal of this work was to evaluate the potential of using log-log and Blasingame production graphs to quickly compare the effectiveness of various stimulation and completion techniques. Based on this very early work the application of Blasingame graphs to qualitatively evaluate stimulation effectiveness appears promising. The conclusions from the reservoir simu

Figure 37. Blasingame graph for horizo

signatures are listed below. One general observation from this work is that production signatures alone may provide important qualitative insights into stimulation and completion effectiveness in shale-gas reservoirs.

ntal Well A

½-slope unit-slope

Intersection ocross-over

r

Figure 37. Blasingame graph for horizontal Well A

½-slope unit-slope

Intersection ocross-over

r

Figure 38. Blasingame graph for horizontal Well B

¼-slope

Figure 38. Blasingame graph for horizon

¼-slope

al Well Bt

18 SPE 124843

However, combining production signatures with microseismic mapping, fracture treatment pressure behavior, geologic and stress information, and matrix permeability measurements should result in a much more quantitative evaluation and significantly more insight into stimulation and completion effectiveness.

• Low conductivity and high conductivity networks can be distinguished using the Blasingame type curve for vertical

and horizontal wells. The production signatures for both vertical and horizontal wells with fracture networks are not materially different.

ty increases and network size and block size decrease, the time to reach “apparent” boundary

imited to

at XL gel treatments in vertical wells resulted in moderate to

. The

treatments. The inference from

res.

ture

if the primary

high conductivity primary fractures

ct the diagnostic signature.

ed to other shale-gas plays

blication of this paper. he authors would also like to thank Dr. Tom Blasingame for his review of this work.

• Production signatures for network fracture flow appear to be different than that of a single planar fracture, possibly providing a technique to distinguish simple and complex fracturing in different gas-shale reservoirs and for different stimulation designs.

• Matrix permeability, network size, and network block spacing do not materially impact the production signatures, but these parameters can significantly affect the duration of various elements of the production signature.

• As matrix permeabilidominated flow decreases. This is due to flow convergence at the boundaries of the network and within the matrix blocks, giving the appearance of reservoir boundaries. In many shale-gas reservoirs the drainage area can be lthe area that was effectively stimulated. And the effective “limits” of network stimulation and drainage efficiency are a function of network and primary fracture conductivity.

• Combining the time to reach “apparent” boundary dominated with MS mapping data and matrix permeability measurements may provide insights into network block size and the effectiveness of various stimulation designs.

• The production for core area Barnett wells indicated thhigh conductivity fractures, with ~60% of the XL treatments showing high conductivity production signatures. In one case, the production signature for the XL gel treatment showed little or no evidence of a fracture networkinference from the production signatures is that the XL gel treatments resulted in small, low conductivity fracture networks that were connected to a relatively high conductivity primary fracture.

• The production signatures for water-frac re-frac treatments in vertical wells showed a combination of moderate to low conductivity fracture networks, with 60% low conductivity signatures and no high conductivity signatures. However, the water-frac re-fracs almost universally improved production compared to the XL gelthe production signatures is that the water-frac re-frac resulted in significantly larger and more complex fracture networks and the increased fracture surface area more than offset the moderate to low fracture conductivity.

• The production signatures for horizontal Barnett completions (all water-frac stimulations and transverse orientation, except one) showed primarily moderate conductivity fracture networks, with 70% of the Horizontal completions exhibiting moderate conductivity production signatures and the remaining exhibiting low conductivity signatu

• The one longitudinally oriented horizontal Barnett well, which was the lone horizontal example of a XL gel treatment and a water-frac re-frac, showed low conductivity production signatures for both treatments.

• It may not be possible to distinguish high uniform conductivity networks from a high conductivity primary fracthat is connected to a low conductivity network (for vertical and horizontal wells).

• It may not be possible to distinguish a low conductivity network from a high conductivity networkfracture has an infinite conductivity (relative to the network).

• It may not be possible to identify or distinguish between the effective lengths of that range from 25% to 100% of the network extent (in the primary fracture direction). However, uncertainty in length of a high conductivity primary fracture does not materially affe

• The ability to apply production signatures to evaluate stimulation performance in shale-gas reservoirs appears promising, but the evaluation needs to be expanded to a larger area, more diverse completions, and more wells in the Barnett shale to better understand its application. In addition, this work needs to be expandto evaluate its applicability to a wider range of reservoir, fracture, and geologic characteristics.

Acknowledgements The authors would like to thank Carbo Ceramics and StrataGen Engineering for supporting the puT

SPE 124843 19

Nomenclature Bcf = 109 standard cubic feet, L3 bbl = barrels, L3 bpm = barrels/min, L3/t cg = gas compressibility, (M/LT2)-1 h = net pay, L k = permeability lb, Klb = pounds, 1000 pounds, M md = 10-3 Darcy, L2 MS = microseismic Mscf/d = 1000 standard cubic feet per day, L3/t OGIP = Original gas in place, L3 ppg = pounds of proppant added per gallon of fluid, M/L3

q = production rate, L3/t Q = cumulative production, L3 SRV = Stimulated reservoir volume, L3

Tcf = 1012 standard cubic feet, L3

TOC = Total organic carbon xf = hydraulic fracture wing or half-length, L xn = hydraulic fracture network width (from microseismic event pattern), L XL = cross-linked ΔP = pressure drop, specifically flowing bottom hole pressure minus reservoir pressure, M/LT2 Δxs,Dx = orthogonal fracture spacing, L μg = gas viscosity, M/LT SI Metric Conversion Factors

acre x 4.046 873 e+03 = m2 bbl x 1.589 874 e-01 = m3 cp x 1.0 e-03 = Pa.s ft x 3.048 e-01 = m oF (oF – 32)/1.8 = oC lbm/gal x 1.198 264 e+02 = kg/cm2 psi x 6.894 757 e+00 = kPa

References Agarwal, R.G, et al: "Analyzing Well Production Data Using Combined-Type-Curve and Decline-Curve Analysis Concepts", SPE

Reservoir Eval. & Eng., (Oct. 1999) Anderson, D.M. et al.: "Production Data Analysis––Challenges, Pitfalls, Diagnostics," paper SPE 102048 presented at the 2006 SPE

Annual Technical Conference and Exhibition, San Antonio, Texas, 24-27 September. Blasingame T.A. et al :" Decline Curve Analysis for Variable Pressure Drop/Variable Flow rate Systems", paper SPE 21513, (1991) Cinco-Ley, H. and Meng, H.-Z. : "Transient Pressure Analysis: Finite Conductivity Fracture Case Versus Damaged Fracture Case",

paper SPE 10179, (1981) Cipolla, C.L., Warpinski, N.R., Mayerhofer, M.J., Lolon, E.P., and Vincent, M.C.: “The Relationship Between Fracture Complexity,

Reservoir Properties, and Fracture Treatment Design,” paper SPE 115769 presented at the 2008 SPE Annual Technical Conference and Exhibition, Denver, Colorado, 21-24 September.

Cipolla, C.L. Lolon, E.P, Mayerhofer, M.J., and Warpinski, N.R.: “Fracture Design Considerations in Horizontal Wells Drilled in Unconventional Gas Reservoirs,” paper SPE 119368 presented at the SPE Hydraulic Fracturing Technology Conference held in The Woodlands, Texas, USA, 19–21 January 2009.

Cipolla, C.L. Lolon, E.P, Mayerhofer, M.J., and Warpinski, N.R.: “The Effect of Proppant Distribution and Un-Propped Fracture Conductivity on Well Performance in Unconventional Gas Reservoirs,” paper SPE 119366 presented at the SPE Hydraulic Fracturing Technology Conference held in The Woodlands, Texas, USA, 19–21 January 2009.

Crafton, J.W. and Anderson, D.: "Use of Extremely High Time-Resolution Production Data to Characterize Hydraulic Fracture Properties," paper SPE 103591 presented at the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24-27 September.

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England, K.W., Poe, B.D., and Conger, J.G.: "Comprehensive Evaluation of Fractured Gas Wells Utilizing Production Data," paper SPE 60285 presented at the 2000 SPE Rocky Mountain Regional/Low Permeability Reservoirs Symposium, Denver, Colorado, 12-15 March.

Fetkovich, M.J.: "Decline-Curve Analysis Using Type Curves", JPT (June 1980)

20 SPE 124843

Fisher, M.K., Davidson, B.M., Goodwin, A.K., Fielder, E.O., Buckler, W.S., and Steinberger, N.P.: “Integrating Fracture Mapping Technologies to Optimize Stimulations in the Barnett Shale,” paper SPE 77411 presented at the 2002 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 29-October 2.

Fisher, M.K., Heinze, J.R., Harris, C.D., Davidson, B.M, Wright, C.A., and Dunn, K.P.: “Optimizing Horizontal Completion Techniques in the Barnett Shale Using Microseismic Fracture Mapping,” paper SPE 90051 presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, Texas, September 26-29.

Frantz, J.H., Jr. et al.: “Evaluating Barnett Shale Production Performance Using an Integrated Approach,” paper SPE 96917 presented at the 2005 SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 9-12.

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Maxwell, S.C., Urbancik, T.I., Steinsberger, N.P., and Zinno, R.: “Microseismic Imaging of Hydraulic Fracture Complexity in the Barnett Shale,” paper SPE 77440 presented at the 2002 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 29-October 2.

Mayerhofer, M.J., Lolon, E.P., Youngblood, J.E., and Heinze, J.R.: “Integration of Microseismic Fracture Mapping Results with Numerical Fracture Network Production Modeling in the Barnett Shale,” paper SPE 102103 presented at the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 24-27.

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SPE 124843 21

Appendix A Blasingame and McCray [1991, SPE 21513] defined a constant rate equivalent time function given as the ratio of the cumulative and the flow rate:

)()(

tqtQte = ................................................. (1)

For gas, Eq. 1 (material balance pseudo-time function) is defined as

τ

μ

τμd

pcp

qtq

ct

t

g

ige ∫=

0 __)()(

)()(

)( .............. (2)

The (pseudo-pressure drop) normalized rate function is given by Eq. 3:

))()(/(/ pmpmqpq i −=Δ ...................... (3) Eq. 4 defines the (pseudo-pressure drop) normalized rate integral function:

∫ −=Δ et

iei d

pmpmq

tpq

0 ))()(()(1)/( ττ ..... (4)

The (pseudo-pressure drop) normalized rate integral-derivative function is given by:

( )[ ]ie

id pqtd

dpq Δ=Δ /)ln(

)/( ( )[ ]iee

pqtdd

tΔ−= /1 …… (5)

For Blasingame plot, Eqs. 3, 4, and 5 are plotted versus the material balance pseudo-time function (te). Using a similar approach, Eq. 6 gives the pseudo-pressure drop normalized pressure integral function

∫−

=Δ et ie

i dq

pmpmt

qp0 )(

))()((1)/( ττ

..... (6)

The pseudo-pressure drop normalized pressure derivative integral is defined as

( )[ ie

id qptd

dqp /)ln(

)/( Δ=Δ ] ................ (7)

Eqs. 6 and 7 are plotted versus te on log-log scale with a unit slope line indicating the boundary dominated flow. For the log-log plot, the reservoir kh may be determined from the derivative stabilization level.

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