SPE-169004-MS

Understanding Shale Performance: Performance Analysis Workflow with Analytical Models in Eagle Ford Shale Play Orkhan Samandarli, SPE; Beth McDonald, SPE; Gervasio Barzola, AAPG; Mark Murray, SPE; and Perry Richmond, SPE, Pioneer Natural Resources

Copyright 2014, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Unconventional Resources Conference – USA held in The Woodlands, Texas, USA, 1-3 April 2014. 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 Tight oil and shale gas resources produced 29% of total US crude oil and 40% of total US natural gas in 2012 (EIA). Technically recoverable quantities of shale gas and shale oil resources for the US are 665 trillion cubic feet and 58 billion barrels, respectively. Considering the impact of the “unconventional boom” on the economy, it is crucial to understand the production performance of wells to maximize the recovery from shale plays. The latest advances in Rate Transient Analysis (RTA) provide quick yet robust tools to assess the quality of the Stimulated Rock Volume (SRV) and long term performance of the wells by estimating EURs. The most common challenge in history- matching of production in shale gas/oil wells has been the non-uniqueness of the history-matched parameters. A lot of emphasis has been put on estimation of fracture half-length, which is believed to be a primary driver for the performance of shale gas/oil wells. Since linear flow is the main transient flow regime in the early life of a hydraulically-fractured shale gas/oil well, a Rate Normalized Pressure (RNP) versus Square Root of Time plot is the most commonly used diagnostic plot for the performance analysis of the wells. A*sqrt(k) or xf*sqrt(k) parameter groups are reported as a proxy for productivity in hydraulically fractured shale/gas oil wells. Besides having permeability as an unknown, the history-match is also sensitive to net hydraulic fracture height, which is one of the inputs to models that must be specified from other sources of information. This paper presents a novel approach for production performance analysis of shale gas/oil wells, which significantly reduces the non-uniqueness issues that one can have in comparison of performance. Twenty two Eagle Ford Shale wells were analyzed across the trend from lean gas to high-yield condensate to define a workflow that could be applied to other wells in different geologic areas, yet provide consistent comparison of long term performance (EURs). Introduction Recent advancements in technology of multi-stage hydraulic-fracturing unlocked the potential of shale plays such as the Haynesville, Eagle Ford, Niobrara, and Wolfcamp shales. In this paper we discuss the performance of Eagle Ford shale wells producing wet gases and condensates. However, the workflow described in this paper can be applied to any gas shale play with slight modifications, and can be adapted to the analysis of oil shale production. Analysis of tight formations that are produced through hydraulic fracturing dates back to the work of Wattenbarger et al. (1998). They used analytical solutions with constant rate or constant pressure inner boundary conditions to analyze wells with long-lasting linear flow. Many other authors published different improvements in the literature since then using their work as a starting point. One important assumption they had in early models was drainage limited to the hydraulically-fractured area only, i.e. flow into the tip of the fractures is ignored due to the low matrix permeability of the rock. Later, contributions beyond the linear flow regime were discussed by various authors (Andersen et al. 2010; Samandarli et al. 2011b; Song et al. 2011). Fig. 1 shows conceptually the various flow regimes that may be seen in the life of a horizontal, multi-stage fractured shale well.

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Figure 1. Flow regimes conceptually to be seen during life of the horizontal multi-stage fractured shale well. Adopted after Song et al. (2011). Ye et al. (2013) comprehensively summarized the work investigating the flow regimes that may occur after the transient fracture linear flow. They also discussed possible geometries (after Chu et al. 2012) that could be created during stimulation depending on rock mechanics and frac design (Fig. 2). They concluded that the geometries that were observed most commonly in the Bakken formation were the Connected and Isolated SRV models. In this paper we are going to refer to these geometries as Composite (for Connected SRV) and Enhanced Frac Region (EFR) (for Isolated SRV) models.

Figure 2. Possible SRV/Fracture patterns adopted after Chu et al. 2012. In this paper we are going to refer to A. Composite SRV Model, B. Enhanced Fracture Region (EFR), where flow beyond the fracture tip is taken into consideration, and C. Bi-wing Fractures model, where no flow beyond the tip of fracture is assumed. Composite (A) and EFR (B) models have two permeability regions: k1 is the permeability inside the enhanced zone and k2 is the matrix permeability. Bi-wing frac model (C) has only one perm (k1), which is the permeability of the matrix. Since the transient formation linear flow is the most dominant transient flow regime during the early life of many horizontal multi-stage fractured wells, a lot of emphasis has been put on calculating A*sqrt(k) or xf*sqrt(k) parameter groups to characterize or compare well performance (Al-Ahmadi et al. 2010; Anderson et al. 2010; Samandarli et al. 2011a). In our study of Eagle Ford wells we observed a poor correlation between production-performance and xf*sqrt(k). In sections below we describe our workflow emphasizing the quantification and well-to-well variability of the observed SRV. For further clarification, we warn readers that the SRV values reported in this paper refer to effective (or conductive) stimulated rock volume that contributes to the production at the time of analysis. SRV values reported in this paper should not be confused with numbers calculated from fracture-stimulation modeling or from analysis of microseismic event volumes.

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Overview of Dataset A total of 22 horizontal wells within a 15x100 miles area completed in the SW to NE-trending Eagle Ford formation, and having more than 2 years of production were investigated (Fig. 3). Fluid types for the wells in this study ranged from dry gas to rich condensate. Reservoir fluids were dew point systems in all wells that were analyzed. The depth of the target zone ranges from 12,500 ft to 14, 450 ft TVD. Initial reservoir pressure gradients varied from 0.73 psi/ft to 0.88 psi/ft. Water production has declined significantly during the flowback, and it is believed no significant formation water is produced. In addition, with the reservoir pressure considerably higher than the dew point pressure, hydrocarbon flow in the system is assumed to be single phase gas. All analyzed wells were completed with the “plug and perf” method. Average stage lengths and cluster spacing were 350 ft and 70 ft, respectively. All analyzed wells were completed with 40/80 ceramic proppant. Average job sizes and injection rates were 800 lbs/ft and 70 bpm, respectively. The completion parameters on these wells were kept constant to appraise geologic responses in different areas. It was proved to be a best practice, because “sweet spots” were identified early in the field life, and prioritized for development. During the development stage, completion parameters were optimized, but the analysis of completion optimization tests is not discussed in this paper.

Figure 3: Area that was investigated during analysis

Performance Analysis Workflow This section explains the workflow that was used to analyze 22 Eagle Ford wells. Once data is loaded to commercial software and quality checked, a series of diagnostic plots are created to discern flow characteristics. The Blasingame Typecurve Analysis is based on methodology initially proposed by Palacio and Blasingame (1993), and has been used by Song et al. (2011) and Samandarli et al. (2012) to analyze the volume of reservoir at pseudo-boundary-dominated flow. The Flowing Material Balance (FMB) plot is an alternative tool to estimate the volume during pseudo-boundary-dominated flow. The advantage of the FMB plot is that when multiple straight lines are observed, then multiple volumes can be calculated to estimate the drainage volumes of the well at different stages of life. During this study we observed that wells produced more than 3 years in general displayed 2 different straight lines. The first straight line corresponds to depletion inside the effective SRV (also known as flush zone, propped zone, enhanced zone), and the second straight line describes the drainage volume caused by pseudo-boundary-dominated flow (PBDF) due to the permeability contrast between the stimulated and non-stimulated reservoir. This PBDF volume may or may not expand with time depending on proppant and reservoir properties (Fig. 4). Later, it is explained why estimating volumes from the FMB plot is important. The third diagnostic plot that we evaluate is a Square Root of Time plot. Many authors used this plot to estimate xf*sqrt(k), as initially proposed by Wattenbarger et al. (1998). This method has been proven to work well where extended linear flow periods are observed. The ease of use of this method arises from the linear geometry, where it is assumed that end of transient linear flow is produced by the no-flow boundaries between adjacent bi-wing fractures. Eq. 1 (Wattenbarger et al. 1998) is the

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equation for the time to the end of linear flow for constant pressure production.

( )it

ehse c

ktyφμ

1591.0= …………………………………………………………………………………..………………….. (1)

Figure 4: Flowing Material Balance Plot (FMB): Two different drainage volumes are observed during life of the well.

Figure 5: Square Root of Time Plot of Well 4

Another advantage of the Square Root of Time plot is that well productivity can be estimated for transient linear flow by calculating the slope of the straight line. Once the slope is calculated, xf*sqrt(k) is calculated by using Eq. 2. Quite a few authors including Samandarli et al. (2011a) reported that the xf estimate is a non-unique when only the linear flow period is observed. When the end of linear flow is observed, then both k (from Eq. 1) and xf (from Eq. 2) are calculated independently. .

( ) ⎭⎬⎫

⎩⎨⎧

=hm

Tc

xkCPit

f φμ4.315

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

Boulis et al. 2013 showed that wells with different xf have the same time to end of linear flow, when all other parameters are the same. They implied that the time to the end of linear flow depends on fracture spacing and permeability as it is shown in

End of the Linear Flow

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Eq. 1. Since the time to the end of linear flow is read from the plot, calculation of k is highly dependent on the fracture spacing assumption. We often assume that every perforation cluster creates one fracture and the volume between adjacent clusters is fully stimulated. But the reality is that the no-flow boundary between two fractures might be shorter if fracture complexity does not extend fully between adjacent clusters. In addition, not all clusters may create individual fractures. These unknowns make permeability estimates more uncertain. Once the data are analyzed with diagnostic plots, we history-match the observed production using analytical models. First model we use is the Bi-Wing fractures model, which has the same geometry and assumptions as the Square Root of Time diagnostic plot. One would expect k and xf calculated from the Square Root of Time plot should honor the pressure history. Fig. 6 is the pressure and rate plot created from the k and xf calculated from Square Root of Time plot of Well 4, which is shown above in Fig. 5. Unfortunately, the k and xf calculated from Square Root of Time plot is not a good match, and none of the wells we analyzed gave a good match with this process.

Figure 6: Pressure and Rate plot by using xf and k calculated from Square Root of Time plot. Bi-wing fracture model with no flow beyond the tip of the fractures. Besides the corrections that Nobakht et al. (2011) mentioned, Eq. 1 and Eq. 2 should not be used to estimate k and xf for production with both pressure and rate varying. Straight line analysis methods that do not use superposition functions cannot be used to estimate varying rate and pressure production, since the derivation of the equations is based on either a constant rate or constant pressure production assumption. Instead, analytical models based on superposition principles should be used to history-match varying rate and pressure data.

Figure 7: Production history of Well 9.

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For flow regime identification, we therefore propose to use the material balance time function, which provides rate-superposition with respect to time. Samandarli et al (2012) explained the advantages of using material balance time for wells where both rates and flowing pressures change with time. We would like to warn readers to be extra careful when using material balance time. In general, the smooth, flowing portion of a well’s production should be used in the analysis. Rate fluctuations due to operating changes, unstable flow, liquid loading, etc. should be excluded from the analysis. Fig. 7 shows the production of Well 9, as an example well going to unstable flow due to liquid loading in November of 2011. Gas and condensate rates were stabilized after the installation of plunger-lift in late March of 2013. In cases like Well 9, we recommend to use the data before the beginning of liquid loading only. Fig. 8 shows the log-log plot of Pressure Normalized Rate vs. Material Balance time for this well.

Figure 8: Flow regime identification with Material Balance time. Liquid loading portion of the production is filtered from analysis (greyed points). Well 8 has quarter slope (bilinear flow) at early time, which corresponds to clean-up period. It is expected that fracture system still has transient gradient during this time. Bilinear flow is followed by half slope (linear flow), which transitions to unit slope (depletion of SRV). First volume calculated from FMB corresponds to this period. As discussed above, k and xf should be estimated by history-matching of production data with analytical models. If a successful match is obtained with the Bi-wing model, then the production honors the assumed geometry. However, in Eagle Ford wells, where the time to the end of linear flow is less than 3 months, other geometries (models) give a better history-match to the production data. This could be explained with the argument that created geometry during stimulation has more complexity than bi-wing fractures. The second model that we evaluated is the Composite model, which is explained in Fig. 2. Wells matched with this model have shorter frac half-lengths due to full coverage between the clusters. From other surveillance data, such as radioactive tracers, chemical tracers, microseismic monitoring, and offset frac pressure monitoring it was concluded that frac half-lengths are longer than the ones estimated with the Composite model. Lastly, we applied EFR model, where another unknown is added into the history-match, namely the width of the enhanced zone for each fracture. Fig. 9 shows the history-match of Well 4 with the EFR model. The EFR model gives the best match, but it also has more degrees of freedom. Therefore, our workflow still includes the history-match with Composite model as a necessary step before moving to EFR model, to verify the need to use an effective enhanced-zone width that is less than the cluster spacing.

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Figure 9: Pressure and Rate plot after History-match with Enhanced Frac Region Model, where there are two permeability zones and flow beyond the tip of the fractures is taken into account. In summary, the workflow that we have described to analyze Eagle Ford wells is:

1. Load the production data, petrophysics, PVT, wellbore schematics, etc. into commercial RTA software and calculate bottomhole flowing pressures if wellhead pressures are used as input.

2. Create a Square Root of Time plot and identify the time to the end of linear flow (if present). Estimate k and xf from this plot. Identify the flow regimes from a log-log plot of Pressure Normalized Rate (PNR) and time. It is recommended to use material balance time function instead of actual time for flow regime identification. Bilinear flow will show ¼ slope, linear flow will show ½-slope, and pseudo-boundary-dominated flow will show close to unit-slope. Transitional flow regimes between linear and pseudo-boundary-dominated flow will have slope between ½ and 1.

3. Create a FMB plot and identify straight lines. If more than one straight line is detected, calculate volumes for each line. We call the volume calculated from the first straight line the Initial Drainage Volume from FMB, also known as volume calculated from depletion of the (effective) SRV (Fig. 4, see above nomenclature).

4. Build and history-match a Bi-wing fracture model. Most Eagle Ford wells will not give good history-match with Bi-wing fractures indicating that created geometry has more complexity. Since the time to end of linear flow is less than 3 months in these wells, a pseudo-boundary-dominated flow regime exists for the wells with production data more than a year. Samandarli et al. (2011a) showed that when two flow regimes are observed, xf and k can be calculated with multi-parameter regression. Therefore, the Bi-wing fracture history-match should be limited to the period prior to pseudo-boundary-dominated flow (Fig. 10). The permeability calculated from this match is used as k1 in next model.

Figure 10: Matching early production of Well 7 with Bi-wing fractures model to estimate k1 (estimated to be 0.0032 md).

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5. The next step is creating a Composite model and history-matching xf using k1 calculated from the Bi-wing model. For k2 we constrain values based on core data analysis. In this case we limited k2 to: 1 < k2 < 50 nD.

6. We use EFR as a final analytical model to history-match the whole production history, and to run forecasts. There are four unknowns to match in this model as explained above. We use k1 calculated from Step 4 as known parameter. If external information is available for fracture half-length, such as well spacing from multi-well pad, or interpretation based on radioactive tracers, chemical tracers, microseismic data or frac modeling, we limit the fracture half-length as well. The lower limit for fracture half-length is the xf value calculated from the Composite model. The width of the enhanced zone per fracture (xi) is another parameter to match, with an upper limit that is equal to fracture spacing, and lower limit of zero. Multi-parameter regression is run on k2, xf, and xi. Once a reasonable match is obtained, a forecast is run for 30 years with limits on abandonment pressure and economic rate.

Discussion of Results In this section we discuss the observations and results from the study. Most of the optimizations done in the field are related to increasing the Net Present Value (NPV) and Rate of Return (ROR) on investment of the assets. Both of these parameters are highly sensitive to EUR of the well for the same hydrocarbon yield area. If we know EUR for each well in a certain geologic area with a certain completion, we can run different well spacing scenarios and optimize the field development. Since EUR is the most important parameter that reservoir engineers report, it is the final product in our workflow. All of our discussions below will focus on what makes a difference in EUR and how we can predict it. In their regional Eagle Ford performance analysis study, Clarke et al. (2014) reported that best-performing wells are located in areas with higher reservoir pressure ( >10,000 psi), and organic rich and less clay prone (brittle) facies. All wells analyzed in this study have the same completion design, as noted above. Therefore, variation in EUR is due to the response of the different rock facies to hydraulic stimulation. The methodology explained in this paper helps to identify the “sweet spots” during appraisal and to capitalize on them by identifying optimum completion designs during the development stage. Since we are going to use EURs calculated from the RTA workflow, we would like to check it against the EURs estimated from Decline Curve Analysis (DCA). In this way we will confirm that EURs calculated by the proposed workflow are within reasonable range of other estimates. Fig. 11 shows the comparison. There is 78% correlation between two compared EURs, with a near-unitary relationship.

Figure 11: Comparison of EURs from proposed workflow and DCA.

The first diagnostic plot that we examined was the Square Root of Time plot, and we showed that xf and k calculated from this methodology does not give good history-match for Eagle Ford wells. But values of xf*sqrt(k) calculated from Square Root of Time plots have been used as a performance indicator widely in the literature (Al-Ahmadi et al. 2010; Anderson et al. 2010; Samandarli et al. 2011a). Therefore, we plotted forecasted EURs against this parameter group to see how well they correlate (Fig. 12). There is only 39% correlation between EUR of the wells and xf*sqrt(k) calculated from Square Root of Time plots. Next, we plotted forecasted EURs against xf*sqrt(k1) calculated from Bi-wing fracture model history-matches (Fig. 13). Poor correlation is observed between EURs and xf*sqrt(k1). This is not surprising, since wells did not give good history-matches with Bi-wing fracture models. From these observations we concluded that xf*sqrt(k) should not be used as a primary parameter for estimating Eagle Ford well performance, because the effective geometry is not similar to bi-wing fractures.

y = 1.0494x ‐ 132.45R² = 0.78

0

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0 1,000 2,000 3,000 4,000 5,000 6,000

EUR

from

RTA

(MM

scf)

EUR from DCA (MMscf)

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So, given rock and fluid properties, “What is the key driver for the performance of Eagle Ford wells?” Since EUR represents performance in our analysis, we plotted it against different history-matched parameters: permeability inside the SRV (k1), permeability outside of the SRV (k2), fracture half length, and history-matched SRV. Fig. 14 shows cross-plots of EUR vs. sqrt(k1) and EUR vs. sqrt(k2). There is again poor correlation between EUR and sqrt(k1), and no correlation is observed between EUR and sqrt(k2). This indicates that the contribution from the matrix within 30 years is going to be insignificant in perm ranges less than 50 nD (Samandarli et al. 2011b). A weak correlation is observed between EUR and fracture half-length either (Fig. 15).

Figure 12: EUR of the analyzed wells do not correlate well with xf*sqrt(k) calculated from Square Root of Time plot, i.e. end of linear flow

Figure 13: EUR of the analyzed wells correlate better with xf*sqrt(k1) calculated from History-matched model.

On the other hand, there is a good correlation between EUR and history-matched SRV (Fig. 16). Can we estimate EUR without going through history-matching? Since performance is strongly correlated to SRV, and we can estimate the drainage volume from FMB plot, we can reasonably estimate history-matched SRV without history-matching. Fig. 17 shows the correlation between volumes inside the history-matched SRV and initial drainage volume estimated from FMB; there is 95%

y = 1,005x + 1,235R² = 0.39

0

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6,000

0.0 0.5 1.0 1.5 2.0 2.5 3.0

EUR

(MM

scf)

xf*sqrt(k) from Square Root of Time Plot

y = 269x + 1,779R² = 0.45

0

1,000

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6,000

0.0 2.0 4.0 6.0 8.0 10.0 12.0

EUR

(MM

scf)

xf*sqrt(k1) from History Match

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correlation! Then we plotted initial drainage volume estimated form FMB against forecasted EUR (Fig. 18). Having a good correlation between volumes from FMB and EUR makes the proposed workflow very efficient and easy to apply. Literally, in “10 minutes” per well one can input PVT, petrophysics, and production data, and build an FMB analysis to compare groups of wells, and to rank them based on performance.

Figure 14: Left: EUR vs. sqrt(k1). Right: EUR vs sqrt(k2)

Figure 15: EUR vs. xf from EFR model. Poor correlation exists between history-matched xf and EUR.

y = 2023.7ln(x) + 10203R² = 0.39

0

1,000

2,000

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5,000

6,000

1.E-02 2.E-02 3.E-02 4.E-02 5.E-02 6.E-02

EUR

(MM

scf)

sqrt(k1): Permeability inside SRV (md1/2)

y = 362.2ln(x) + 4936.9R² = 0.032

0

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0.E+00 2.E-03 4.E-03 6.E-03 8.E-03

EUR

(MM

scf)

sqrt(k2): Permeability outside of SRV (md1/2)

y = 8.1505x + 1705.3R² = 0.23

0

1,000

2,000

3,000

4,000

5,000

6,000

0 50 100 150 200 250 300

EUR

(MM

scf)

Fracture Half Length from EFR Model (ft)

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Figure 16: Good correlation was observed between forecasted EUR of the well and history-matched SRV.

Figure 17: Excellent correlation was observed between Volume inside history-matched SRV and Initial Drainage Volume from FMB – “10 minutes method”.

Figure 18: From combining two plots above we have good correlation between EUR and FMB – “10 minutes method”.

Another topic we would like to cover is the importance of reporting volumes rather than frac half-lengths. Although frac half-length is extremely important in multi-well analysis for well spacing studies and field development, it is almost impractical to calculate frac half-length uniquely due to the uncertainty of created frac heights. In the discussion above we showed that there is 75% correlation between history-matched SRV and EUR (Fig. 16). If we look at the correlation between EUR and

y = 0.97x + 1,028R² = 0.75

0

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0 1,000 2,000 3,000 4,000 5,000

EUR

(MM

scf)

Volume inside History Matched SRV (MMscf)

y = 0.87x + 53.3R² = 0.95

0

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0 1,000 2,000 3,000 4,000 5,000

Volu

me

insi

de H

isto

ry M

atch

ed S

RV

(MM

scf)

Initial Draiange Volume from Flowing Material Balance (FMB)

y = 0.897x + 985R² = 0.80

0

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5,000

6,000

0 1,000 2,000 3,000 4,000 5,000

EUR

(MM

scf)

Initial Draiange Volume from Flowing Material Balance (FMB)

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estimated fracture half-lengths (Fig. 15) there is only 23% correlation. This is not surprising considering all these wells had different net fracture heights (input) and different widths of enhanced zone around each fracture (history-matched). To verify that the history-match is affected by the volume and surface to flow only, we history-matched one of the analyzed wells (Well 5) in a numerical simulator (Fig. 19). In the work of Clarke et al. (2014), Well 5 is in the far NE of the trend and corresponds to Tier III, poor performing wells with high clay content (>32%) and lower brittleness. For history-matching, we used a model in which the enhanced zone around each fracture is widest close to wellbore and diminishes as it goes away from the wellbore. Fracture height was 240 ft.

Figure 19: History-match of Well 5. Upper-left is geometry that used in numerical simulation. Only 6 fractures were stimulated and rates are adjusted based on symmetry element assumption. Upper-right is the production data for Well 5. Lower-left is the oil match, lower-middle is water match, and lower right is bottomhole pressure match where gas rate was used as control during history-match. From the history-match, the fracture half-length was estimated to be 132 ft, with a volume of 1.38 Bscf inside the created SRV. These values are typical for wells in Tier III (Clarke et al. 2014). This SRV agrees well with what we estimated from the FMB plot that corresponds to depletion of SRV (Fig. 20). We then kept all history-matched parameters and SRV the same, but reduced the frac height by half and doubled the fracture half-lengths and re-ran the model for the history-match period and for the 30 years forecast period. In both cases the area to flow and the volume of the created SRV are the same.

Figure 20: FMB plot of Well 5. Volume corresponding to depletion of SRV is 1.46 Bscf. Using Correlation from Fig. 17 yields: y=0.87*1460+53.3 = 1,323 MMscf (or 1.32 Bscf of History-matched SRV).

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1

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0 50 100 150 200 250 300 350 400 450

FBHP

Rates

Producing Days

Oil Water Gas FBHP

SPE-169004-MS 13

Both cases have identical history-matches for bottomhole flowing pressure and 30 years EUR forecast. This exercise shows that frac half-length should not be used alone as the key parameter to characterize performance of the field. On the other hand, effective SRV volume combines all the unknowns: frac half-length, frac height and stimulated width of the enhanced zone per fracture into one parameter, which gives better correlation with EUR.

Figure 21: Comparison of two different frac half lengths and heights. Although frac half length was 2 times more and frac height half of the first case, both cases gave identical history-match for bottomhole pressure and quite similar gas EUR forecast for 30 years (4% difference). Impact on Field Development The proposed workflow discussed in this paper will help operators both in appraisal and development stages of field life. During the appraisal phase when the completion parameters are usually kept the same we can compare the response of the rock by comparing the volumes calculated from FMB. At this stage, the value of effective SRV calculated from this workflow will represent the rock quality, such as high organic content and brittleness. For the shale plays where the duration of the linear flow is few months and boundary-dominated behavior is observed quickly, we can leverage FMB to estimate the drainage volumes at the time of the analysis. Identifying “sweet spots” in the early life of the field will help operators to make more informed acquisition and strategic development decisions, which will increase the overall value of their assets. Once “sweet spots” are identified, completion programs may be varied to find the best recipe for each of the various rock types. Effective SRV volumes calculated at this stage will represent the relative effectiveness of the tested completion designs given that rock quality was the same for the tested region. A final step in the development of shale play is to optimize the well spacing. From Fig. 21 we saw equally good matches can be obtained with different half-lengths and heights. Having 132 ft or 264 ft of frac half-length did not affect the history match, but it will certainly have great impact on the field development. Same argument is true for height; an operator would like to drain reservoir effectively both vertically and laterally. Although the workflow presented in this paper is a great tool to estimate the effectiveness of stimulation and reservoir quality, knowing geometry of the drained reservoir is crucial for optimum field development. Conclusions In this paper, we described an RTA workflow that was designed during analysis of wells from Eagle Ford shale. Reservoir fluid varied from dry gas to rich condensate. The completion design was kept the same in all analyzed wells. Below are the main conclusions reached during this study:

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• Two or more straight lines can be observed in an FMB plot: first corresponds to depletion of the SRV, the second is due to slow expansion of pseudo-boundaries between stimulated and non-stimulated rock.

• Calculating k from the end of linear flow equation and xf from Square Root of the Time plot is not recommended due to possible violations of assumptions: varying rates and pressures, not having bi-wing geometry.

• It is recommended to analyze flow regimes in the log-log plot of Pressure Normalized Rate vs. Material Balance time.

• Both Composite and EFR models gave good history-match with production data. The EFR model is recommended to use for forecasting and EUR prediction.

• Poor correlation is observed between EUR and either xf or xf*sqrt(k). On the other hand, good correlation exists between EUR and the history-matched SRV or the Initial Drainage Volume from FMB.

• It was shown that different combinations of xf and frac height will give the same history-match and long-term forecast, as long as the SRV and the area to flow are kept constant.

• It was concluded that xf cannot be solved independently if external information about the height of the fractures is not available. SRV is the only parameter that can be solved uniquely.

• The proposed workflow will benefit operators both in appraisal and development stages of field life by helping identify “sweet spots” and optimal completions.

Nomenclature A = Surface area cross sectional to the flow, ft2 bpm = Barrels per minute ct = Total compressibility, 1/psi h = Formation thickness, ft k = Permeability, md mCP = Slope of straight line on Pressure Normalized Rate vs. sqrt(t) plot at constant flowing pressure

condition, psi2-day0.5/cp-Mscf sqrt(k) = Square root of permeability, md0.5 SRV = Stimulated rock volume T = Temperature, R tehs = End of half slope (linear flow) TVD = True vertical depth, ft xf = Fracture half-length, ft ye = Half of fracture spacing, ft Greek Symbols φ = Porosity μ = Viscosity, cp References Anderson, D.M., Nobakht, M., Moghadam, S., and Mattar, L. 2010. Analysis of Production Data from Shale Gas Wells.

Paper SPE 131787 presented at SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, USA. Boulis, A., Jayakumar, R., Nyaaba, C., Rai, C., and Sahai, R. 2013. Challenges Evaluating Shale Gas Well Performance:

How do we account for What We Don’t Know? Paper SPE 16396 presented at International Petroleum Technology Conference held in Beijing, China.

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Clarke, P.R., Portis, D.H., Barzola, G.J., Bello, H., and Basu, N.K. 2014. Assessing Well Performance in a Prolific Liquids- rich Shale Play – An Eagle Ford Case Study. AAPG Memoir. The Eagle Ford Shale: A Renaissance in U.S. Oil Production. In press.

Nobakht, M., Clarkson, C.R., and Kaviani, D. 2011. New and Improved Methods for Performing Rate-Transient Analysis of Shale Gas Reservoirs. Paper SPE 147869 presented at SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, Indonesia.

Palacio, J.C., and Blasingame, T.A. 1993. Decline Curve Analysis Using Type Curve Analysis of Gas Well Production Data. Paper SPE 25909 presented at the Joint Rocky Mountain Regional and Low Permeability Reservoirs Symposium, Denver, Colorado, USA.

SPE-169004-MS 15

Samandarli, O., Al-Ahmadi, H., and Wattenbarger R.A. 2011a. A New Method for History-matching and Forecasting Shale Gas Reservoir Production Performance with a Dual Porosity Model. Paper SPE 1144335 presented at SPE Northern American Unconventional Gas Conference and Exhibition, The Woodlands, Texas, USA.

Samandarli, O., Al-Ahmadi, H., and Wattenbarger R.A. 2011b. A Semi-Analytical Method for History-matching Fractured Shale Gas Reservoirs. Paper SPE 144583 presented at the SPE Western Regional Meeting, Anchorage, Alaska, USA.

Samandarli, O., Valbuena E., and Ehlig-Economides C. 2012. Production Data Analysis in Unconventional Reservoirs with Rate-Normalized Pressure (RNP): Theory, Methodology, and Applications. Paper SPE 155614 presented at SPE Americas Unconventional Resources Conference, Pittsburgh, Pennsylvania, USA.

Song, B. and Ehlig-Economides, C. 2011. Rate Normalized Pressure Analysis for Determination of Shale Gas Well Performance. Paper SPE 144031 presented at SPE Northern American Unconventional Gas Conference and Exhibition, The Woodlands, Texas, USA.

Wattenbarger, R.A., El-Banbi, A.H., Villegas, M.E., and Maggard, J.B. 1998. Production Analysis of Linear Flow into Fractured Tight Gas Wells. Paper SPE 39931 presented at SPE Rocky Mountain Regional/Low Permeability Reservoirs Symposium and Exhibition, Denver, Colorado, USA.

Ye, P., Chu, L., Harmawan I., and Williams, M. 2013. Beyond Linear Flow Analysis in an Unconventional Oil Reservoir. Paper SPE 164543 presented at Unconventional Resources Conference, The Woodlands, Texas, USA.