ogip spe 152066 pa

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Resource Evaluation for ShaleGas Reservoirs

Z. Dong, SPE, S.A. Holditch, SPE, and D.A. McVay, SPE, Texas A&M University

Summary

Many shale gas reservoirs have been previously thought of assource rocks, but the industry now finds these source rocks stillcontain large volumes of natural gas and liquids that can be pro-duced by use of horizontal drilling and hydraulic fracturing. How-ever, one of the most uncertain aspects of shale gas developmentis our ability to accurately forecast gas resources and shale gas de-velopment economics. The uncertainty of the problem begs for aprobabilistic solution.

The objective of our work was to develop the data sets, meth-odology, and tools to determine values of original gas in place(OGIP), technically recoverable resources (TRR), recovery factor (RF), and economic viability in highly uncertain and risky shalegas reservoirs. Existing approaches for determining values of TRR, such as the use of decline curves or even volumetric analy-

ses, may not be reliable early on because there may not be enoughproduction history for decline curves to work well or the uncer-tainty in the reservoir properties may be too large for volumetricanalyses to be useful.

To achieve our research objective, we developed a computer program, Unconventional Gas Resource Assessment System(UGRAS). In the program, we integrated Monte Carlo techniquewith an analytical reservoir simulator to estimate the original vol-ume in place, predict production performance, and estimate thefraction of TRR that are economically recoverable resources(ERR) for a variety of economic situations. We applied UGRASto dry gas wells in the Barnett shale and the Eagle Ford shale todetermine the probabilistic distribution of their resource potentialand economic viability. On the basis of our assumptions, theEagle Ford shale in the dry-gas portion of the play has more tech-

nically recoverable resources than the Barnett shale. However, theEagle Ford shale is currently not as profitable as the Barnett shalebecause of the higher drilling costs in the Eagle Ford dry-gaswindow.

We anticipate that the tools and methodologies developed inthis work will be applicable to any shale gas reservoir that has suf-ficient data available. These tools should ultimately be able toallow determination of technically and economically recoverableresources from shale gas reservoirs globally.

Introduction

Many gas shale plays are currently under development in the USoil and gas industry. The use of horizontal drilling in conjunction

with hydraulic fracturing has greatly expanded the ability of pro-ducers to profitably produce natural gas from low-permeabilitygeologic formations, particularly shale formations. We have pre-viously analyzed 15 basins in North America where shale gasresources have been evaluated, and the results have been pub-lished (Dong et al. 2011). The total volume of original shale gasin place for those 15 North American basins was estimated at4,774 to 7,341 Tcf. It is clear that there are abundant volumes of natural gas in North America. The question we now need to an-swer is what portion of the gas resource is technically and eco-nomically recoverable. The objective of our work was to develop

the data sets, methodology, and tools to determine values of

OGIP, TRR, RF, and economic viability in highly uncertain andrisky shale gas reservoirs.

Petroleum Resources Management System (PRMS). Theterms “resources” and “reserves” have previously been used, andcontinue to be used to represent various categories of mineraland/or hydrocarbon deposits. In March 2007, SPE, the AmericanAssociation of Petroleum Geologists, the World Petroleum Coun-cil, and the Society of Petroleum Evaluation Engineers jointlypublished the PRMS to provide an international standard for clas-sification of oil and gas reserves and resources (Fig. 1a). It is im-portant to remember that the broadest categories are also the leastprecise. As one moves toward the categories at the bottom of thechart, the associated estimates of the amount of natural gas in

those categories become more and more uncertain. However,TRR and ERR are not formally classified in the system.

Energy Information Administration (EIA) Classification

System. According to the EIA, TRR are the subset of the totalresource base that is recoverable with existing technology. Theterm “resources” represents the total quantity of hydrocarbonsthat are estimated, at a particular time, to be contained in knownaccumulations and accumulations that have yet to be discovered(prospective resources). ERR are those resources for which thereare economic incentives for production. It is important to notethat, at some time in the future, economically unrecoverableresources may become recoverable, as soon as the technology toproduce them becomes less expensive or the characteristics of themarket are such that companies can ensure a fair return on their investment by extracting the resources. For our purposes, we con-sidered TTR to be the resources that can be produced within a 25-year time period.

We rearranged categories of PRMS and present an overviewof how the estimates of TRR and ERR are broken down (Fig. 1b).Those commercial resources, including cumulative productionand reserves, are ERR. TRR are the subset of the total resourcebase that includes commercial resources, contingent resources,and prospective resources. Estimated ultimate recovery is not aresources category, but a term that refers to the quantities of pe-troleum that are estimated to be potentially recoverable from anaccumulation, including those quantities that have already beenproduced. For the remainder of this paper, we consider only natural-

gas resources.

Monte Carlo Probabilistic Approach. Shale gas plays are gen-erally characterized by low geologic risk and high commercialrisk. Uncertainty exists in geologic and engineering data and, con-sequently, in the results of calculations made with these data.Probabilistic approaches are required to provide an assessment of uncertainty in resource estimates.

Decline-curve analysis (DCA) is commonly used for future-performance prediction and resource estimation when productiondata are available. However, several studies (e.g., Ilk et al. 2008)have pointed out that the Arps method provides optimistic solu-tions for unconventional reservoirs because boundary-dominatedflow (assumed in the Arps DCA model) is not reached within rea-

sonable times in these reservoirs. Some authors have appliedprobabilistic approaches to DCA to quantify the uncertainty

CopyrightVC 2013 Society of Petroleum Engineers

This paper (SPE 152066) was accepted for presentation at the SPE Hydraulic FracturingTechnology Conference, The Woodlands, Texas, USA, 6–8 February 2012, and revised forpublication. Original manuscript received for review 9 February 2012. Revised manuscriptreceived for review 30 October 2012. Paper peer approved 3 December 2012.

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resulting from use of these imperfect methods (Jochen and Spivey1996; Cheng et al. 2010).

Reservoir simulation coupled with stochastic methods (e.g.,Monte Carlo) has provided an excellent means to predict produc-tion profiles for a wide variety of reservoir characteristics and pro-ducing conditions. The uncertainty is assessed by generating alarge number of simulations, sampling from distributions of uncer-tain geologic, engineering, and other important parameters. Thistopic has been an object of study for some time in conventionalreservoirs (MacMillan et al. 1999; Sawyer et al. 1999; Nakayama2000). However, few applications to unconventional reservoirscan be found in the literature. Oudinot et al. (2005) coupled Monte

Carlo simulation with a fractured reservoir simulator, COMET3,to assess the EUR in coalbed-methane reservoirs. Schepers et al.(2009) successfully applied this Monte Carlo/COMET3 procedureto forecast EUR for the Utica shale.

Analytical Model. Given the complex nature of hydraulic-frac-ture growth, the extremely low permeability of the matrix rock inmany shale gas reservoirs, and the predominance of horizontalcompletions, reservoir simulation is commonly the preferredmethod to predict and evaluate well performance. Analytical solu-tions for fluid flow in naturally fractured reservoirs were publishedby Warren and Root (1963) and Kazemi (1969). Semianalyticalsolutions for hydraulically fractured horizontal wells in fracturedreservoirs have been published (Medeiros et al. 2008). PMTx

(PMTx version 2.0 2010), with a number of modeling options,such as the transient dual-porosity reservoir model (Kazemi 1969),is an analytical unconventional-gas-reservoir simulator designedto quickly and easily model single-well, single-phase gas produc-tion on the basis of near-wellbore reservoir performance under specified well-completion scenarios. One of the important applica-tions of PMTx 2.0 is to estimate ultimate gas recovery for horizon-tal wells with transverse fractures in a rectangular shale gasreservoir.

Economics Determination. Almadani (2010) presented a meth-odology to determine the percentage of TRR that is economicallyrecoverable from the Barnett shale as a function of gas price andfinding and development costs (F&DC). For ERR, he applied eco-

nomic criteria of minimum 20% internal rate of return (IRR) andmaximum 5-year payout to recover the initial investment, which

are hurdles commonly required by investors in the oil and gasindustry. The author suggested that if you could not achieve pay-out in 5 years or less, the well would not be a wise investment.

We developed UGRAS to help us determine the values of ERR. In the program, we integrated Monte Carlo simulation withan analytical reservoir simulator, PMTx 2.0, to estimate the origi-nal volume in place, predict production performance, and estimatethe fraction of TRR that are ERR for a variety of economic situa-tions. In the following sections, we present the workflow of UGRAS and apply UGRAS to assess the resource potential andevaluate economic viability of gas in the Barnett shale and in thedry-gas window in the Eagle Ford shale.

Methodology

Shale gas reservoirs are highly heterogeneous, and well productiv-ity depends on reservoir properties as well as completion andstimulation parameters. Even if finite-difference reservoir simula-tors are available, it can be time consuming to perform a large res-ervoir-simulation study. In our study, we applied UGRAS togenerate gas-production profiles for a variety of reservoir, well,and hydraulic-fracture scenarios. Thousands of simulations can beautomatically run to explore combinations of unknown reservoir and well parameters across their ranges of uncertainty. We use theinvestment hurdles of IRR more than 20% and payout time lessthan 5 years, applied on an individual-well basis, to determine thefraction of TRR that is ERR for a variety of economic situations.In future analyses, we plan to conduct sensitivity studies on thesevalues of IRR and payout. However, for this paper, we have madethe assumption that if a well does not pay out in 5 years or less, itis probably not worth drilling at this time. Other places to drilland spend capital should be more profitable.

The workflow of our probabilistic reservoir model UGRAS isoutlined in Fig. 2. First, an input file is created and uncertainparameters are assigned probability distributions. There is no li-mitation to the number of parameters that can be varied. The dis-tributions are typically normal, uniform, triangular, exponential,or log-normal. These distributions are sampled for volumetricanalysis and flow simulation to determine OGIP, TRR, and RF.Then, these steps are repeated many times to generate frequencyand cumulative density plots for OGIP, TRR, and RF. Finally,

economic analysis was run to calculate the production from wellsthat meet economic criteria (IRR more than 20% before federal

1P 2P 3P

PROVED

Range of Uncertainty

UNRECOVERABLE S u b - C O M M E R C I A L

D I S C O V E R E D P I I P

U N D I S C O V E R E D

P I I P

PROSPECTIVE

RESOURCES

Low Best High

UNRECOVERABLE

T o t a l O i l & G a s R

e s o u r c e B a s e ( P I I P )

C O M M E R C I A L PRODUCTION

I n c r e a s i n g C h a n

c e o f C o m m e r c i a l i t y

RESERVES

CONTINGENT

RESOURCES

1C 2C 3C

1P 2P 3P

PROVED

Range of Uncertainty

UNDISCOVERED

I n c r e a s i n g C h a

n c e o f C o m m e r c i a l i t y

1C 2C 3C

PROSPECTIVERESOURCES

Low Best High

CONTINGENT

RESOURCES

PRODUCTION

RESERVES

T e c h n i c a l l y

U n r e c o v e r a b l e

DISCOVERED

T o t a l O i l & G a

s R e s o u r c e B a s e

U n d i s c o v e r e d

S u b -

E c o n o m i c a l

T e c h n i c a l l y R e c o v e r a b l e

(a) Resource Classification of PRMS (b) EIA definitions mapped to PRMS categories

TRR

ERR

OGIP

PROBABLE POSSIBLE

C O M M E R C I A L

PROBABLE POSSIBLE

Fig. 1—Flow chart and generalized division of resource and reserves categories. (a) Resource classification of PRMS. (b) EIA defi-

nitions mapped to PRMS categories.

6 January 2013 SPE Economics & Management

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income tax, payout less than 5 years) over production from all

wells according to different F&DC.

Shale-Gas-Reservoir Model

Today, shale gas reservoirs are typically developed with horizon-tal wells that are hydraulically fractured with multiple stages. Asmore knowledge is gained through microseismic monitoring of these fracture treatments, it appears that they are more likely cre-ating a network of fractures. Thus, shale reservoirs may behave asa transient dual-porosity system, with the secondary porosity sys-tem (matrix) contributing to the primary porosity system (system)consisting of the created fracture network and possible existingnatural fractures. Transient dual-porosity systems have been usedto model naturally fractured reservoirs (Kazemi 1969; Swaan

1976). This model can also be used for modeling shale gas reser-voirs where multistage-fracture completions have created the frac-ture network. In the transient dual-porosity model, there are twotransients—one moving through the fracture system and the other moving through the matrix toward the interior of the matrixblocks.

The transient dual-porosity model is characterized by the stor-ativity ratio and the interporosity flow coefficient. The storativityratio, x, is the fraction of pore volume (PV) in the fractures com-pared with the total PV (Eq. 1). The interporosity flow coefficient,k, is proportional to the ratio of permeabilities between the matrixand the fractures (Eq. 2), and it determines the timing and magni-

tude of the contribution from the matrix to the fractures. A largevalue indicates that fluids flow easily between the two porousmedia, whereas a small value indicates that flow between themedia is restricted. We could not find any literature reporting thevalues of k and x for gas shales. The storativity ratio is usually inthe range of 0.01 to 0.1; we assumed a value of 0.01 to be repre-sentative of shales because of small PV of the fractures. The inter-porosity flow coefficient for dual-porosity reservoirs is usually inthe range of 10 –4 to 10 –8 (Fekete Associates Inc. 2012). The rangebetween 10 –6 and 10 –8 is assumed to be representative of shalesbecause of the large contrast between the permeabilities of the

fractures and the matrix. The outer boundary is defined as a closedrectangle, and the well is centered in the drainage area. Table 1summarizes the reservoir model used for shale-gas-reservoir simulation.

x ¼ð/ct Þ f

ð/ct Þ f þ ð/ct Þmð1Þ

k ¼ 4nðn þ 2Þr 2w

L2k m

k f ðFor slab blocks; n ¼ 1:Þ ð2Þ

ResourceDistribution of Barnett Shale

The Barnett Shale produces primarily dry gas. In this work, wehave considered only gas production and have not included anywells that may be in the oil window. Vertical wells were firstdrilled in the Barnett shale in the early 1980s, but development of the Barnett shale play was not seriously considered until almost2 decades later with the advent of horizontal drilling in 2003(Fig. 3). As of December 2011, the Barnett shale play had morethan 12,561 wells, including 9,449 horizontal wells and 3,112 ver-tical wells. More than 8,270 Bcf of gas has been produced, of which 75% is from horizontal wells.

Completion History of Horizontal Wells. Horizontal wellboresare typically oriented northwest/southeast to take advantage of

. . . . . . . . . . . . . . . . . . . . . . .

. . . . . .

Define simulation input file

Monte Carlo Sampling forreservoir properties

Run reservoir simulation

(PMTx2.0)

Economic viability analysis(ERR/ERR vs gas price etc.)

Probabilitydistribution

of TRR

Probabilitydistribution of

OGIP

Define probability densityfunction for uncertainreservoir properties

Probabilitydistribution

of RF

Volumetric Analysis

Fig. 2—Flow chart of UGRAS.

TABLE 1—RESERVOIR MODEL FOR SHALE GAS PLAYS

Specifications Descriptions

Porosity Transient dual porosity, slab blocks

Fracture conductivity Infinite

Inner boundary Horizontal TransFracs

Outer boundary Rectangle

Lithology Shale

Pressure step Constant

Permeability Isotropic

Well location Centered

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

0

500

1000

0

2500

5000

7500

10000

P r o d u c i n g W e l l s

1500

2000

Vertical Well Count

Vertical Well Production

Horizontal Well Production

Horizontal Well Count

P r o d u c t i o n ,

B c f

Fig. 3—Production has rapidly increased in the Barnett shale by horizontal wells (HPDI 2011).


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natural productivity in the Barnett shale (Hale 2010). Laterallengths increased from approximately 3,000 ft in 2003 to as longas 8,965 ft in 2009, with an average value of 4,000 ft (Powell2010). Typical fracture half-length of the Barnett shale is between300 and 400 ft, with seven to nine fracture stages (Kennedy2010).

Initially, cemented liners and multistage fracturing techniqueswere used in the Barnett shale (Fig. 4a). This type of completioninvolves cementing casing in the horizontal wellbore and by useof “plug-and-perf” stimulation. The inherent costs of multipleinterventions with coiled tubing, perforating guns, and deploy-

ment of fracturing equipment needed for each stage are extremelyhigh, not to mention inefficient consumption. Production by useof this method can also be limited, because cementing the well-bore closes many of the natural fractures and fissures that wouldotherwise contribute to overall production (Lohoefer et al. 2010).

Between 2004 and 2006, a new openhole, multistage system(OHMS) completion technology (Fig. 4b) was run in DentonCounty, Texas (Lohoefer et al. 2006). This type of completion, onaverage, performed better than the cemented completion method(Lohoefer et al. 2010). The major advantage of OHMS is that allthe fracture treatments can be performed in a single, continuouspumping operation without the need for a drilling rig, savingcosts.

Reservoir Parameters. Both thickness and reservoir pressureincrease in the southwest/northeast direction, implying a signifi-cant increase in potential production rate from southwest to north-east. The thickness of the Barnett shale ranges from 100 to 600 ft(Hayden and Pursell 2005; Grieser et al. 2008). Pressure gradientis in the overpressured category, typically 0.53 psi/ft (Lafolletteet al. 2012). The most common reservoir pressure used for Barnettshale reservoir simulation is 3,000 to 5,000 psi (Chong et al.2010). The average reservoir temperature is 200F (TransformSoftware & Services 2011). The average porosity in productiveportions of the Barnett shale ranges from 4 to 5% (Hayden andPursell 2005). Published values for average permeability have

variously been reported to be 0.00007 to 0.005 md (Grieser et al.2008). Productive, organic-rich portions of the Barnett shale aver-age 25 to 43% water saturation (Bruner and Smosna 2011). Theorganic matter in the shale was first reported to contain 60 scf/ton,but this could be as high as 125 scf/ton (Montgomery et al. 2005).It is reported that typical well spacing in the Barnett shale is 60 to160 acres (Hayden and Pursell 2005). Table 2 summarizes thereservoir parameters reported from literature for the Barnett shale.In our modeling, we used a reservoir size of 4,800 1,000 ft (111acres/well), with a lateral length of 4,000 ft, fracture half-lengthof 350 ft, and 10 stages. Table 3 shows the fixed reservoir param-

eters used for the Barnett shale single-well reservoir simulations.

Model Verification. We used the HPDI (2011) database as our source for production data. Since 2004, 1,492 horizontal wells inthe Barnett shale have been completed and produced more than60 months. Initially, uniform distributions were assigned for sixkey uncertain parameters—net pay, initial pressure, permeability,porosity, water saturation, and gas content (Table 4)— honoringtheir reported ranges from the literature documented in Table 2.We did not consider possible correlation among these parameters.The distributions for three of these uncertain parameters—netpay, permeability, and gas content—were refined until a reasona-ble match between simulated and actual 5-year cumulative pro-duction was obtained (Fig. 5). We did not have to make large

changes in these distributions to achieve the match. Table 4also summarizes the final distributions for the six uncertainparameters.

The red curve in Fig. 5 shows the distribution of 5-year cumu-lative production from the 1,492 horizontal wells. The blue curveis the distribution from 1,000 random realizations of 5-year cumu-lative production simulated by UGRAS with the parameters inTable 3 and the final distributions in Table 4.

As a further check of the model, we compared simulated andactual production-decline trends. We calculated the average wellmonthly gas production from the 1,492 wells over the first 60months (Fig. 6). From the probabilistic model described in Tables3 and 4, we plotted well production curves corresponding to the

(a) Cemented-liner, multi-stage fracturing method(Initial Barnett shale well completion)

(b) Open-hole, multi-stage system (OHMS) completion(Latest Barnett shale well completion)

Fig. 4—Lower-damage, more-intensively-stimulated horizontal-well completions (Kuuskraa 2009). (a) Cemented-liner, multistage-fracturing method; initial Barnett shale well completion. (b) OHMS completion; latest Barnett shale well completion.

TABLE 2—SUMMARY OF RESERVOIR PARAMETERS

REPORTED FROM LITERATURE FOR THE BARNETT SHALE

Parameter Range

Net pay (ft) 100–600

Porosity (%) 4–5

System permeability (md) 0.00007–0.005

Water saturation (%) 25–43

Gas content (scf/ton) 60–125

Depth (ft) 6,500–8,500

Reservoir pressure (psi) 3,000–5,000

Well spacing (acres) 60–160

Vitrinite reflectance (%) 0.6–1.6

TOC* (%) 2.4–5.1

* TOC ¼ total organic carbon.

TABLE 3—FIXED PARAMETERS FOR THE BARNETT

SHALE SIMULATION

Layer Data Value

Reservoir temperature (F) 200

Flowing bottomhole pressure (psia) 500

Reservoir length (ft) 4,800

Reservoir width (ft) 1,000

Fracture half-length (ft) 350

Horizontal-well length (ft) 4,000

Number of fracture stages 10

k (dimensionless) 7 10 –7

x (dimensionless) 0.01

Bulk density (g/cm3) 2.5


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mean, P10, P50, and P90 5-year cumulative production values(Fig. 6). The simulated production curves were run out to 25 yearsto observe the long-term production trends. Both the actual andsimulated production profiles show high initial production ratewith steep production decline in the first year and significantdecrease in the decline rate in subsequent years.

OGIP, TRR, and RF. Detailed geologic and reservoir data wereassembled to establish the free gas and adsorbed gas in place for the Barnett shale. Free gas becomes the dominant in-placeresource for deeper, higher-clastic-content shales. Eq. 3 is used tocalculate the free gas in place for shale gas reservoirs:

Free GIP ¼ 43; 560 Ah/ð1 SwÞ

Bgið3Þ

where Bgi ¼ 0:02329 ZT

pIn addition to free gas, shale can hold significant quantities of

gas adsorbed on the surface of the organics in the shale formation.Adsorbed gas can be the dominant in-place resource for shallow,organic-rich shales. A Langmuir isotherm is established for the

prospective area of the basin by use of available data on TOC andthermal maturity to establish the Langmuir volume ( V L) and pres-sure ( P L).

Adsorbed gas in place (GIP) is then calculated by use of Eq. 4:

Adsorbed GIP ¼ 43; 560 AhqcGcð1 /Þ ð4Þ

where Gc ¼ V L p

P L þ p.. . . . . . . . . . . . . . . .

. . . . . . . . . . .

TABLE 4—INITIAL AND FINAL DISTRIBUTIONS FOR UNCERTAIN BARNETT SHALE PARAMETERS

Parameters

Initial Uniform Distributions Final Distributions

Minimum Maximum Distribution Type l r Minimum Median Maximum

Net pay (ft) 100 600 Log-normal 200 50 — — —

Initial pressure (psi) 3,000 5,000 Uniform 3,000 5,000

System permeability (md) 0.00007 0.005 Log-normal 0.0005 0.0005 — — —

Water saturation (fraction) 0.25 0.43 Uniform — — 0.25 — 0.43

Porosity (fraction) 0.04 0.05 Uniform — — 0.04 — 0.05

Gas content (scf/ton) 60 125 Triangular — — 60 100 125

Loglogistic (c, b, a) ¼ log-logistic distribution with location parameter c, scale parameter b, and shape parameter a.

Lognorm (l, r) ¼ log-normal distribution with specified mean and standard deviation.

0 0 1 2

5-year Cumulative Production, Bcf/well

Field Data Simulation Result

3 4 5

102030405060708090

100

P e r c e n t i l e ,

%

Fig. 5—Probability distribution of cumulative-gas-production (5-year) match result for the Barnett shale.

0 50 100 150 200 250 300

Month

1.E+03

1.E+04

1.E+05Field Data From Barnett Shale

Simulated by UGRAS(P90)

Simulated by UGRAS(Mean)




M c f / M o n t h

Fig. 6—Average Barnett shale gas production of 1,492 wells overlaid by the simulated TRR distribution.


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The probability distributions for OGIP, TRR, and RF are gen-erated by reservoir parameters in Table 2 and finalized densityfunctions in Table 3. The P10, P50, and P90 values of OGIP are8.4, 12.2, and 17.8 Bcf/111 acres, respectively (Fig. 7).

We chose a 25-year production history rather than 30 or 40

years. In the current economic environment, no one really careshow much gas will be on the books 40 years from now. However,in future work, we plan to vary the well life to determine whether it really makes a difference in terms of both TRR and ERR.

The simulation results yielded a TRR distribution with a P10value of 1.1 Bcf/111 acres, a P50 of 2.2 Bcf/111 acres, and a P90of 4.5 Bcf/111 acres (Fig. 8). The distribution is quite wide, indi-cating significant uncertainty in forecasting Barnett shale gas pro-duction. RF of Barnett shale also follows a log-normaldistribution (Fig. 9), with P10, P50, and P90 values of 10, 18, and35%, respectively.

Economic Evaluation. We next examined the economic impactof different gas prices and F&DC on ERR in the Barnett shale. To

do this, for each realization we determine the gas price required to just meet the economic hurdles for particular F&DC. The eco-nomic analysis is performed at the assumptions listed in Table 5.Gas shrinkage results from the usage of a percentage of producedgas for mechanical compression along the pipeline.

Ranking the realizations, we can determine the fraction of TRR that is economically recoverable for a particular combina-tion of gas price and F&DC. We then repeat this to determine theratio of ERR/TRR over a range of gas prices and F&DC (Fig. 10).With typical F&DC of USD 3 million for Barnett shale wells anda gas price of USD 4/Mcf, 20% of the Barnett shale gas TRR iseconomically recoverable. If we increase the gas price, we willincrease the fraction of technically recoverable resource that iseconomically recoverable.

With an estimated acreage of 3.2 million acres and assumed

well spacing of 111 acres, 29,000 wells could be drilled in theBarnett shale. Thus, the resource potential for the entire Barnettshale is estimated at 352 Tcf of OGIP (P50), 63 Tcf of TRR(P50), and 12 Tcf of ERR (20% of TRR) at USD 4.0/Mcf gasprice and USD 3 million/well F&DC (Table 6).

5 50OGIP, Bcf

OGIP Simulated by UGRAS

Loglogistic (1.5,10.6,5.3)10%

0%

20%

30%

40%

50%

60%

70%

80%

90%

100%

P e r c e n t i l e

Fig. 7—Probabilistic distribution of OGIP/111 acres for the Bar-nett shale.

0%0.4 4

TRR, Bcf

TRR Simulated by UGRAS

Lognorm(2.5,1.5,Shift(0.05))

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

P e r c e n t i l e

Fig. 8—Probabilistic distribution of TRR/111 acres with a 25-year life for the Barnett shale.

0%0 20 40 60 80 100

Recovery Factor, %

Simulated by UGRAS

Lognorm(20,10,Shift(0.3))10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

P e r c e n t i l e

Fig. 9—Probabilistic distribution of RF with a 25-year life for theBarnett shale.

TABLE 5—ECONOMIC PARAMETERS FOR BARNETT SHALE

Operating Cost USD/Mcf

Working interest 100%

Royalty burden 25%

Severance taxes 7%Gas shrinkage 6%

10

0.2

0.4

0.6

0.8

1

E R R / T R R ,

B c f / B c f

10

Gas Price, $/Mcf

F&DC = 1, MMS

F&DC = 2, MMS

F&DC = 3, MMS

F&DC = 4, MMS

F&DC = 5, MMS

F&DC = 7, MMS

F&DC = 6, MMS

Fig. 10—Ratio of ERR/TRR as a function of gas price and F&DCfor the Barnett shale.

TABLE 6—RESOURCE POTENTIAL FOR THE BARNETT

SHALE PLAY

Category P10 P50 P90

OGIP (Tcf) 242 352 513

TRR (Tcf) 32 63 130

ERR (Tcf) 6 12 26


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ResourceDistribution in the GasWindowof the

Eagle Ford

The Eagle Ford shale in south Texas is in its infancy in terms of development compared with other shale plays in the US. TheEagle Ford shale is directly beneath the Austin chalk. Wells in theEagle Ford shale produce from depths between 4,000 and 14,000ft. The Eagle Ford dips toward the Gulf of Mexico. It is consid-ered to be the source rock for the Austin chalk. The Eagle Ford isbest known for producing variable amounts of dry gas, wet gas,natural-gas liquids, condensate, and oil. In late 2008, the first fewexploration wells in the Eagle Ford were drilled in LaSalleCounty in the gas window of the play (Fig. 11). In this paper, wehave evaluated only the dry-gas portion of the Eagle Ford, whichis also the deepest portion of the play where drilling costs are the

highest. In future work, we plan to evaluate the gas-condensate

window and the oil window in the Eagle Ford. For now, all resultsin this paper were computed by use of gas-production data onlyfrom the dry-gas portion of the Eagle Ford.

In the Eagle Ford shale, there were seven producing gas wellsin 2008 and more than 509 wells producing in 2011 (Fig. 12). Asof December 2011, more than 434 Bcf of dry gas has been pro-duced. Fig. 12 shows dry-gas production from the Eagle Ford shaleis increasing annually. But because of the low gas price, the aver-age daily dry-gas production reached only 854 MMcf/D in 2011.

The average lateral length of gas wells in the Eagle Ford was5,600 ft in 2011 (Fig. 13). Unlike many other shale plays, the

Fig. 11—Eagle Ford extends across south Texas with updip oil, middip condensate, and downdip gas windows.

2008 2009 2010 20110

100

200

300

400Well Count

600

450

300

150

0

W e l l C o u n t

Year

Gas


B c f

Fig. 12—Annual dry-gas production in the Eagle Ford shale(HPDI 2011).

0

2000

2008 2009 2010 2011Year

4000

6000

8000

L a t e r n a l L e n g t h ,

f t

Fig. 13—The trend of average lateral length of Eagle Ford hori-

zontal wells over time (data provided by UnconventionalResources LLC, personal communication with G. Vonieff).


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Eagle Ford shale does not exhibit natural fracturing within the for-mation. The carbonate content of the Eagle Ford can be as high as70%. The high carbonate content and consequently lower claycontent make the Eagle Ford more brittle and easier to stimulatethrough hydraulic fracturing than other shales with less carbonate.

Typical fracture half-length of the Eagle Ford shale is 350 ft, with8 to 10 fracture stages (Kennedy 2010).

Reservoir Parameters. We obtained the ranges of reservoir pa-rameters from 121 horizontal gas wells in the Eagle Ford shale(Table 7). Table 8 lists the fixed parameters used for the EagleFord shale simulation. The case was modeled on a well with 11multistage hydraulic transverse fractures, with fracture half-lengthof 350 ft and a total wellbore length of approximately 5,600 ft,producing natural gas for a period of 25 years. The assumed wellspacing is 147 acres/well.

Model Verification. We used the HPDI database as our sourcefor production data. Since 2010, 152 horizontal wells in the Eagle

Ford shale have been completed and produced for more than 12months. We initially assigned uniform density functions for netpay, initial pressure, water saturation, porosity, permeability, andgas content within their parameter ranges (Table 9). We did notconsider possible correlation among these parameters. These sixdensity functions were refined until a reasonable match betweensimulated and actual 1-year cumulative production was obtained.Table 9 also summarizes the final distributions for the six uncer-

tain parameters.The red curve in Fig. 14 shows the cumulative probability dis-tribution of 1-year cumulative production from the 152 horizontalwells. The blue curve is the cumulative probability distributionfrom 1,000 random realizations of 1-year cumulative productionsimulated by UGRAS with the parameters in Table 8 and finaldistributions in Table 9.

As a further check of the model, we compared simulated andactual production-decline trends. We calculated the average wellmonthly gas production from the 152 wells over the first 12months (Fig. 15). From the probabilistic model described inTables 7 and 8, we plotted well production curves correspondingto the mean, P10, P50, and P90 1-year cumulative production val-ues (Fig. 15). The simulated production curves were run out to 25years to observe the long-term production trends.

Resource Assessment. Figs. 16 through 18 show the probabilitydistributions for OGIP, TRR, and RF generated by reservoir pa-rameters in Table 7 and finalized density functions in Table 8.The values of OGIP range from 7.5 (P10) to 25.3 (P90) Bcf/147acres. TRR for a 25-year recovery period range from 2.3 (P10) to8.5 (P90) Bcf/147 acres. Eagle Ford RF ranges from 25 (P10) to40% (P90). Again, we chose 25 years to determine TRR as a rea-sonable well life that is of interest to us now. In the future, wewill run sensitivity studies to determine whether the answerschange significantly for other values, such as 30 or 40 years.

Economic Evaluation. The economic analysis for the EagleFord shale was performed at the assumptions in Table 10. The

TABLE 7—RESERVOIR PARAMETERS OF THE EAGLE FORD SHALE*

Upper Eagle Ford Lower Eagle Ford

Range Mean Range Mean

Depth (ft) 5,500–14,300 11,700 5,800–14,400 11,800

Net pay (ft) 3–236 100 8–326 163

System permeability (md) 0.0001–0.0005 0.0003 0.0001–0.0007 0.0004

Water saturation (%) 12–44 23 9–44 18

Porosity (%) 3–9 6 3–12 8

Gas content (scf/ton) 7–96 41 18–118 82

Bulk density (g/cm3) 2.44–2.65 2.55 2.36–2.63 2.46

TOC (%) 0.3–4.0 1.9 0.7–5.4 3.6

*Personal communication. 2011. Houston: W.D. Von Gonten & Company.

TABLE 8—THE FIXED PARAMETERS AND RESERVOIR

MODEL FOR EAGLE FORD SHALE SIMULATION

Layer Data Value

Reservoir temperature (F) 247

Flowing bottomhole pressure (psia) 500

Reservoir length (ft) 6,400

Reservoir width (ft) 1,000

Fracture half-length (ft) 350

Horizontal-well length (ft) 5,600

Fracture stage 18

k (dimensionless) 10 –6

x (dimensionless) 0.01

Bulk density (g/cm3) 2.51

TABLE 9—INITIAL AND FINAL DISTRIBUTIONS FOR UNCERTAIN EAGLE FORD SHALE PARAMETERS

Parameters

Initial Uniform Distribution Final Distribution

Minimum Maximum Distribution Type l r a b Shift

Net pay (ft) 3 326 Log-normal 130 50 — — —

Initial pressure (psi) 4,300 10,900 Log-normal 7,200 1,100 — — —

Water saturation (fraction) 0.09 0.44 Gamma 0.17 0.06 3.8 0.03 0.06

Porosity (fraction) 0.03 0.12 Inverse Gaussian 0.1 6.8 — — –0.04

System permeability (md) 0.0001 0.0007 Log-normal 0.0004 0.0001 — — —

Gas content (scf/ton) 7 120 Gamma 49 19 7 7 —

Invgauss (l,

k) ¼ inverse Gaussian distribution with mean l and shape parameter k.

Pearson 5 (a, b) ¼ Pearson-type V (or inverse gamma) distribution with shape parameter a and scale parameter b.


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ERR/TRR ratio in the dry-gas portion of the Eagle Ford shale for different gas prices and F&DC is shown in Fig. 19. With typicalF&DC of USD 9 million and gas price of USD 4/Mcf, only asmall fraction of TRR in the dry-gas portion of the Eagle Ford

shale is economically recoverable. It is clear that either higher gasprices or better technology to increase average well recovery or decrease well costs is required to economically produce the largeamount of natural gas in the dry-gas portion of the Eagle Ford.

Because of the economic environment, it can be observed thatvirtually all current drilling (other than to hold acreage) is occur-ring in the gas-condensate window or the oil window in the EagleFord (Fig. 11). The addition of liquid production to the natural-gas production significantly improves the average product priceand the economic profitability of these Eagle Ford wells. In addi-tion, the gas-condensate and oil portions of the Eagle Ford areshallower than the dry-gas portion; thus, drilling costs are lower in the shallower portion of the play.

In the dry-gas window, the estimated productive acreage is

estimated to be 3 million acres. If we assume an average wellspacing of 147 acres, 20,000 wells could be drilled in the dry-gas

00

102030405060708090

100

P e r c e n t i l e ,

%

0.3 0.6 0.9 1.2

1-year Cumulative Production, Bcf/well

Field Data Simulation Result

1.5 1.8

Fig. 14—Distribution of cumulative production (1-year) match result.

0 50 100 150 200 250 300

Month

1.E+03

1.E+04 P r o d u c t i o n ,

M c f / M o n t h

1.E+05

Field Data From Eagle Ford Shale


Simulated by UGRAS(Mean)



1.E+06

Fig. 15—Average Eagle Ford dry-gas production of 152 wellsoverlaid by the simulated TRR.

0%033

OGIP, Bcf

Simulated by UGRAS

Loglogistic(1.6,12,3)10%20%

30%

40%

50%

60%

70%

80%

90%

100%

P e r c e n t i l e

Fig. 16—Probabilistic distribution of OGIP/147 acres for theEagle Ford shale gas window.

20 30 40 50

Simulated by UGRAS

Pearson5(43,1651,Shift(–7.6))

60 70

Recovery Factor, %

0%

10%

20%

30% P e r c e n t i l e

40%

50%

60%70%

80%

90%

100%

Fig. 18—Probabilistic distribution of RF with a 25-year life forthe Eagle Ford shale gas window.

1 10

Simulated by GURAS

Invgauss(5,17)

TRR, Bcf

10%

0%

20%

30% P e r c e n t i l e

40%

50%

60%

70%

80%

90%

100%

Fig. 17—Probabilistic distribution of TRR/147 acres with a 25-year life for the Eagle Ford shale gas window.

TABLE 10—ECONOMIC PARAMETERS FOR EAGLE

FORD SHALE

Operating Cost USD 1.3/Mcf

Working interest 100%

Royalty burden 25%

Severance taxes 7%

Gas shrinkage 6%


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portion of the Eagle Ford shale. Thus, the resource potential for the entire Eagle Ford dry-gas window is 278 Tcf of OGIP (P50)and 90 Tcf of TRR (P50) (Table 11). The value of ERR will be afunction of the average gas price in the future. Currently, the natu-ral-gas price is approximately USD 4/Mcf or less, which suggeststhat many of these wells are not economic. However, the industryis working to increase natural-gas demand, which should increasethe natural-gas price. As such, much of the TRR will be recoveredwhenever the natural-gas price increases to a point at which moredrilling will occur.

Discussion

The portion of the Eagle Ford shale in the dry-gas window hasmore TRR per well on average than the wells in the Barnett shale,

first because the reservoir pressures are greater. Second, it appearsthat operators generally drill longer laterals with more stages and

use larger fluid and proppant volumes per stage in the hydraulic-fracturing treatments pumped in Eagle Ford dry-gas wells (thus,our assumed well spacing of 147 acres/well for the Eagle Fordgas window vs. 111 acres/well for the Barnett shale). These tworeasons are more likely to have a greater impact in the TRR dif-ference than differences in petrophysical properties betweenbasins (Baihly et al. 2010). Higher reservoir pressure in the EagleFord gas window is the main reason that Eagle Ford has higher RF than Barnett shale.

Table 12 lists opportunities for increasing ERR in both theBarnett shale and Eagle Ford shale gas window by increasing gasprices and/or decreasing F&DC. For instance, by decreasingF&DC cost to USD 2 million/well, 50% of TRR could be eco-nomically recoverable from the Barnett Shale at a gas price of

USD 4/Mcf. If gas price increases to USD 6/Mcf, 25% of TRRcould be recovered economically in the Eagle Ford gas window atan F&DC of USD 9 million/well.

The technology and tools described in this paper can be usefulin assessing TRR and ERR in shale gas plays. However, it is im-portant to acknowledge the assumptions and uncertainties inher-ent in the results presented in this paper. First, we assumed a 25-year well life for calculation of TRR and economic hurdles of IRR of more than 20% and payout time of less than 5 years for calculation of ERR. Although we believe these are reasonable cri-teria and many operators and investors in shale gas plays use simi-lar values, we acknowledge that other operators may use differentcriteria and, thus, may obtain different results.

Our resource assessments are high-level assessments.Although we estimate resources for entire plays, we do not model

reservoir and well properties on a well-by-well basis. Instead, wemodel each play as a whole, by use of probability distributionsthat encompass the variability in reservoir properties across thefield as well as the uncertainty in these properties. For example,the OGIP varies from county to county because of differences innet thickness and other properties across the field. The distributionof net thickness we used in the Barnett study covered the greater net thickness in Tarrant County and the lower net thickness in thesouthwestern Barnett shale. Another limitation of our high-levelassessments is related to vertical variability in properties. We didnot consider vertical variations in properties, such as fracturabil-ity, throughout the zones evaluated. In some areas, the net thick-ness of the shale gas plays is so great that the entire pay zonecannot be completed and produced. However, we used the samedistributions of net pay for the OGIP calculation and TRR predic-tion for the Barnett and Eagle Ford shales in this study.

We also acknowledge the uncertainty in the production fore-casts generated by the probabilistic analytical simulator. The inputparameters used to generate production forecasts for both the Bar-nett and the Eagle Ford gas window were obtained from the litera-ture and well data. Operators and reserves evaluators reviewedthe parameter values and forecasts in these plays to verify their reasonableness. The probabilistic forecasts for the Barnett shalewere calibrated against actual 5-year cumulative production data,although this of course does not guarantee the accuracy of 25-year forecasts. Few performance data exist for the Eagle Ford shale.Even though we calibrated the Eagle Ford dry-gas forecastsagainst actual production data, there is uncertainty in theseforecasts.

The ERR/TRR ratio was calculated on an individual-well ba-sis. That is, ERR/TRR is the TRR from wells that individually

10

0.2

0.4

0.6

E R R / T R R ,

B c f / B c f

0.8

1

10

Gas Price, $/Mcf

F&DC = 6, MMS F&DC = 10, MMS

F&DC = 11, MMS

F&DC = 12, MMS

F&DC = 7, MMS

F&DC = 8, MMS

F&DC = 9, MMS

Fig. 19—Ratio of ERR/TRR as a function of gas price and F&DC

for the Eagle Ford shale.

TABLE 11—RESOURCE POTENTIAL FOR DRY GAS IN THE

EAGLE FORD SHALE PLAY

Category P10 P50 P90

OGIP (Tcf) 153 278 516

TRR (Tcf) 47 90 173

TABLE 12—OPPORTUNITIES FOR INCREASING

THE ERR/TRR RATIO

ERR/

TRR

Barnett Shale Eagle Ford Gas Window

F&DC (USD

106/well)

Gas Price

(USD/Mcf)

F&DC (USD

106/well)

Gas Price

(USD/Mcf)

75% 1 3.1 6 6.5

2 5.1 7 7.2

3 7.1 8 8.3

4 9.0 9 9.05 10.5 10 10.0

6 12.0 11 10.6

50% 1 2.8 6 5.2

2 4.0 7 6.0

3 5.1 8 7.0

4 7.0 9 7.2

5 8.0 10 8.0

6 9.8 11 9.0

25% 1 2.1 6 4.2

2 3.1 7 5.0

3 4.1 8 5.5

4 5.0 9 6.0

5 6.0 10 6.2

6 7.0 11 7.0


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meet the economic hurdles at a particular gas price and particular F&DC divided by the TRR from all wells. This does not accountfor the practice of budgeting and drilling “packages” of wells instatistical shale gas plays. Because there is a lot of variability inindividual shale-gas well performance, a package of wells maymeet the economic hurdles overall, whereas some wells will indi-vidually meet the economic hurdles and some will not. Oncedrilled, wells will be allowed to continue producing to a net-cash-flow economic limit even if they do not meet the economic hur-dles previously specified. Thus, actual ERR/TRR ratios for theBarnett and Eagle Ford gas window are potentially greater thanthe ERR/TRR ratios presented in this paper.

Finally, although a probabilistic reservoir model was used inthis work, the ERR/TRR ratios presented in this paper are deter-ministic and do not capture the uncertainties described in the pre-ceding paragraphs. We plan to address these limitations in futurework.

Conclusions

In this study, we have evaluated resource potential and economicviability in the Barnett shale and the dry-gas window in the EagleFord shale by use of a probabilistic, analytical reservoir model.We conclude the following.1. The median (P50) resource potential of the Barnett shale is

estimated at 352 Tcf of OGIP and 63 Tcf of TRR. The valuefor ERR is a function of F&DC and gas prices.

2. The median (P50) resource potential of the gas window in theEagle Ford shale is 278 Tcf of OGIP and 90 Tcf of TRR. Thevalue of ERR is a function of F&DC and gas prices.

3. The natural-gas window in the Eagle Ford shale has more tech-nically recoverable resources than the Barnett shale. However,the natural-gas window in the Eagle Ford shale will not befully developed until drilling costs are reduced and/or natural-gas prices increase from the current USD 4/Mcf range.

Nomenclature

A ¼ area, acres Bgi ¼ gas formation volumetric factor, cf/scf

ct ¼ total compressibility, 1/psid ¼ fracture spacing, ft

Gc ¼ initial gas content, scf/lbm H ¼ net pay, ftk f ¼ fracture permeability, md

k m ¼ matrix permeability, md L ¼ characteristic length of a matrix blockn ¼ number of flow dimensions, dimensionless

P10, P50, P90 ¼ values for which the probability is 10, 50, or 90% that the value will not be exceeded, indi-cated by the 10th, 50th, or 90th percentile on acumulative probability plot

r w ¼ wellbore radiusSw ¼ water saturation

k ¼ interporosity flow coefficient, dimensionless

l ¼ meanqc ¼ bulk density, lbm/cf r ¼ standard deviation/ ¼ formation porosityx ¼ storativity ratio, dimensionless

Acknowledgments

We thank the Research Partnership to Secure Energy for Americaand the Crisman Institute in the Department of Petroleum Engi-neering at Texas A&M University for supporting this research.We would like to acknowledge William D. Von Gonten withW.D. Von Gonten & Company for providing relevant data setsand valuable feedback to calibrate our research findings and anal-

ysis. We thank John P. Spivey with Phoenix Reservoir Engineer-ing for providing PMTx 2.0 for this research.

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Zhenzhen Dong works as a reservoir engineer with Schlum-berger Consulting Services. She holds a PhD degree in petro-leum engineering from Texas A&M University; an MS degree inpetroleum engineering from Research Institute of PetroleumExploration and Development, PetroChina; and a BS degreein mathematics from Northeast Petroleum University, China.Dong has focused her research in areas involving unconven-tional gas reservoirs, reserves and resource assessment, welltesting, and performance analysis.

Stephen A. Holditch is the Director of the Texas A&M EnergyInstitute, the Director of the Crisman Institute for PetroleumResearch, and a professor of petroleum engineering. He washead of the Harold Vance Department of Petroleum Engineer-ing from January 2004 until January 2012. Holditch was SPEPresident in 2002 and SPE Vice President–Finance and a mem-ber of the Board of Directors 1998–2003. In addition, he servedas a trustee for the American Institute of Mining, Metallurgical,and Petroleum Engineers (AIME) during 1997–98. Holditch hasreceived numerous awards in recognition of his technicalachievements and leadership. In 1995, he was elected to the

National Academy of Engineering and in 1997 to the RussianAcademy of Natural Sciences. In 1998, Holditch was electedto the Petroleum Engineering Academy of Distinguished Grad-uates. He was elected an SPE and AIME Honorary Member in2006. In 2010, Holditch was honored as an Outstanding Gradu-ate of the College of Engineering at Texas A&M University. Hecurrently serves as Editor-in-Chief of SPE’s peer-reviewed tech-nical journals.

Duane McVay is the Rob L. Adams ’40 Professor in the Depart-ment of Petroleum Engineering at Texas A&M University in Col-lege Station, Texas. His research interests include reservoirsimulation, uncertainty quantification, and unconventionalreservoirs. Previously, McVay spent 16 years with S.A. Holditch& Associates, a petroleum engineering consulting firm. He is aDistinguished Member of SPE and earned BS, MS, and PhDdegrees in petroleum engineering from Texas A&M University.

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