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Copyright 1999, Society of Petroleum Engineers Inc.

This paper was prepared for presentation at the 1999 SPE Eastern Regional Conference andExhibition held in Charleston, West Virginia, 21-22 October 1999.

This paper was selected for presentation by an SPE Program Committee following review ofinformation contained in an abstract submitted by the author(s). Contents of the paper, aspresented, have not been reviewed by the Society of Petroleum Engineers and are subject tocorrection by the author(s). The material, as presented, does not necessarily reflect anyposition of the Society of Petroleum Engineers, its officers, or members. Papers presented atSPE meetings are subject to publication review by Editorial Committees of the Society ofPetroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paperfor commercial purposes without the written consent of the Society of Petroleum Engineers isprohibited. Permission to reproduce in print is restricted to an abstract of not more than 300words; illustrations may not be copied. The abstract must contain conspicuousacknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O.Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

AbstractThis paper discusses the development and application of anExcel spreadsheet program for history matching andforecasting gas production from shale wells in the

Appalachian, Michigan, or Illinois basin. The program issimulation-based and uses 1,000 to 3,000 simulation runs to amaximum time of 30 years. The simulation runs are madeusing a matrix of key reservoir parameters specific to the areaof study. The output gas and water production from thesimulation runs is incorporated into a worksheet in such amanner that any single forecast may be easily selected. Theuser selects a combination of input parameters from drop-down menus, and graphs of simulated gas and waterproduction are instantly generated on the main worksheetscreen.

Observed gas and water production data are entered into aseparate worksheet for history matching and are plotted

together with the simulated data. On the main worksheet, thedrop-down menus are used to find the simulation inputparameters that result in the best overall history match. Oncea match is obtained, the user may easily export the 30-yearsimulated gas forecast for economic analysis.

This tool provides a simple method to quickly analyzeshort-term production data when no other means is available.Also, this tool can be easily adapted to different geologicalsettings including shales, coals, and sandstones.

IntroductionOver the past 10 to 15 years spreadsheet software, including

EXCEL™, has evolved to such an extent that it is nowpossible to create very powerful “spreadsheet programs”without tedious line-by-line code development. Theseprograms can include high-quality graphics and complicatedmathematical functions.

With the use of macros and display boxes,1 modern

spreadsheet software provides the capability for a user-friendly interface with little or no programming. Hencecommercial application software can be developed inEXCEL™ or other spreadsheet products.

In reservoir engineering we often history match single-wellproduction data using a reservoir simulator. This involvesfour steps: preparing an ASCII file with time and daily or

monthly rate and cumulative production of each flowingphase, setting up an input dataset containing a description of the reservoir under study, making multiple (often 20 or more)simulation runs with different combinations of parameters, andpreparing graphs of simulated and actual data. This is, at best,a very tedious, time-consuming process.

Once this spreadsheet is set up for a particular geologicsetting, it simplifies the process of history matching. A user-friendly interface is provided to allow the engineer to quicklycompare observed gas and water production to simulated data.A reservoir simulator is not required.

Description of Software

The software we developed is simulation-based andincorporates the results of several thousand simulations into auser-friendly Excel spreadsheet for comparison with actualproduction data. Specific values of key reservoir parameterscontrolling production are defined for making the simulationruns and FORTRAN pre- and post-processors are used to assistin generating the simulator input files and in preparingsimulated production forecasts in a convenient format for usein the spreadsheet.

Each spreadsheet is designed for a specific geologic target.Some properties are fixed for the simulation runs. These

SPE 57439

A Simulation-Based Spreadsheet Program for History Matching and Forecasting ShaleGas ProductionW. K. Sawyer, SPE, Holditch - Reservoir Technologies, M. D. Zuber, SPE, Holditch - Reservoir Technologies, and J. R.Williamson, SPE, Holditch - Reservoir Technologies

http://contents.pdf/

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2 W. K. SAWYER, M. D. ZUBER, J. R. WILLIAMSON SPE 57439

include PVT properties, relative permeability curves and shalematrix porosity and permeability. For each history matchingparameter, two to five values are used that cover the expectedrange of variability.

All combinations of the history matching parameters areused in making the simulation runs. Table 1 shows an

example in which six parameters are varied. The total numberof simulations required is the product of the number of valuesselected for each parameter. The overall process consists of four major components: pre-processing, simulation, post-processing, and importing data into the spreadsheet. Each of these components will be discussed in detail.

TABLE 1 – EXAMPLE OF VARIABLE PARAMETERS

FOR SIMULATION RUNS

Area(acres)

Pressure(psia)

InitialWater

Saturation(percent)

Porosity(percent)

Permeability(md)

NaturalFractureSpacing

(ft)

40 250 100 0.1 1 180 350 80 0.3 2 3

120 450 1.0 4 6

160 550 10 12

20 20

Pre-Processing Module. The first major step in developingthe simulation-based spreadsheet program is to determine a setof input parameters for the simulation runs, as discussedabove. The specific parameters and values for each parameterare defined in an ASCII file (Params.inp) as shown in Table 1.A FORTRAN pre-processor reads the Params.inp file and atemplate input file for the reservoir simulator that is going to

be used. Fig. 1 shows a flowchart of the complete pre-processing module, which is controlled by the BatchCommand File INSIM.BAT. The FORTRAN pre-processoruses the Template.sim file and generates a simulator input filefor each combination of the variable parameters. For example,with the variable parameters shown in Table 1, a total of 2,400 simulator input files would be generated.

Params.inp Fortran

Pre - Processor

aooo1.sim

aooo2.sim

: :

: :

Params.prn

RUNSIM.BAT

OUTSIM.BAT

Simulator Input Files

Template.sim

INSIM.BAT

Fig. 1 – Flowchart of the pre-processing module.

The pre-processor also generates a Params.prn file (Fig. 1)which is designed for input to the spreadsheet program. TheParams.prn file consists of one record for each simulator input

file generated. This record begins with a run number andincludes the variable parameters used for that run. Some keyfixed variables such as shale matrix porosity and permeabilityare also included in this file.

In addition to generating the simulator input files and theParams.prn file, the FORTRAN pre-processor also generates

two large batch files (Fig. 1) that are used for furtherprocessing. The use of these batch files is discussed below.

Simulation Module. The second step in the development of the spreadsheet application is to run the simulator for each of the input files generated. This is accomplished by executingthe batch file RUNSIM.BAT, which is generated by the inputprocessor (Fig. 1). Fig. 2 shows a flowchart of the simulationmodule. When RUNSIM.BAT is executed, the simulator isrun using the first input file, which is named a0001.sim. Eachsimulator input file generated by the pre-processor is executedsequentially by RUNSIM.BAT. This is normally a 3- to 12-hour run, depending on the number of simulations to be run.

The simulator used for the runs in this paper was Shalegas™,2

which gas been used in several studies of shale reservoirsthroughout the U.S.3,4

Reservoir

Simulator

aooo1.sim

aooo2.sim

: :

: :

aooo1.out

aooo2.out

: :

: :

RUNSIM.BAT

Fig. 2 – Flowchart of the simulation module.

Post-Processing Module. Fig. 3 shows the flowchart for thepost-processing module. Each simulation run generates alarge ASCII file (e.g., a0001.out) that contains pressure andsaturation arrays, well reports, summary tables, and othergeneral information regarding the simulation run. This filemust be read and processed to obtain production-versus-timedata suitable for use in the spreadsheet program.

Time.dat Fortran

Post - Processingaooo1.out

aooo2.out

: : : :

Summary.out

Simdata.prn

OUTSIM.BAT

Fig. 3 – Flowchart of the post-processing module.

A FORTRAN program reads the .out files and processes theproduction rate and cumulative production data. The result isa file called Simdata.prn (Fig. 3) which consists of four

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SPE 57439 A SIMULATION SPREADSHEET PROGRAM FOR HISTORY MATCHING AND FORECASTING SHALE GAS PRODUCTION 3

records for each simulation run. These four records containgas and water rate and cumulative production at a set of pre-determined times that are specified in the file Time.dat (Fig.3). The times are closely spaced during the first few monthsand increase to 180 days, giving 91 points of productionversus time data over a 30-year period.

The post-processor also generates a Summary.out file (Fig.3) which consists of one record for each simulation run. Thisfile contains 30-year gas and water production and cumulativematerial balances for each run. The Simdata.prn file is usedfor checking and debugging to be certain that all simulationswere successfully executed. Once post-processing issuccessfully completed, the Simdata.prn (Fig. 3) file and theParams.prn file (Fig. 1) are ready to be imported into thespreadsheet program.

Importing Data Into the Spreadsheet. Fig. 4 shows aflowchart of the Excel spreadsheet program. The Params.prnfile from the pre-processor and the Simdata.prn file from the

post-processor are ASCII text files that are imported intoappropriate worksheets for history matching and forecasting.Figs. 5 and 6 show portions of the worksheets containing theimported data. Fig. 5 shows the first few records of theParams.prn file.

Params.prn Excel Spreadsheet

for

History Matching and

Forecasting

GraphicRate and Cum

PlotsSimdata.prn

Obsdata.prn Forecast.out

Fig. 4 – Flowchart of the spreadsheet program.

Actual production data must also be imported into aworksheet within the spreadsheet program. These data mustconsist of five columns including time, gas and water rates,and gas and water cumulative production. The productiondata usually originate in an ASCII text file that may beimported and pasted into the observed data worksheet. Up to500 rows of observed data may be input as shown in Fig. 6.

The User Interface. The spreadsheet program is used byselecting reservoir parameters for history matching and/orforecasting from drop-down menus on the main worksheet asshown at the top of Fig. 7. All simulation runs are made usinga net pay of 100 ft. An input box for adjusting net pay isprovided at the top right of the main worksheet.

The parameters chosen by the user from the drop-downmenus are used for two purposes. First the specific simulationrun is identified, as shown at the top of the parametersworksheet in Fig. 5. Then the production data for thissimulation are selected from the simulated data worksheet.These simulated data are plotted and compared with actualdata from the observed data worksheet (Fig. 6).

Two graph options are available for plotting simulated andobserved data (Fig. 7). A drop-down menu allows selection of

production rate or cumulative production. Gas and water plotsof simulated and actual production data are presented on thelower part of the main worksheet.

The x- and y-axes may be scaled as desired using standardExcel features. For example, by double-clicking on the y-axisand selecting scale, the y-axis scale may be changed and the

axis type (cartesian or logarithmic) may be specified. Hardcopy plots may be obtained by clicking just inside the graphborder and then clicking the print icon. Also, both plots maybe printed on the same page by simply printing the mainworksheet. Selecting landscape orientation results in a full 8½ by 11-in page that gives the parameter selection and bothgas and water plots as shown in Fig. 7.

The selected simulated data may be exported to a newspreadsheet by clicking on the Export Simulated Data button(Fig. 7). This button executes a macro which converts thesimulated data to a monthly basis for detailed economicanalysis. The exported data are saved in a new spreadsheetcalled Forecast.xls.

A macro is also provided for simple economic analysis.Clicking on the Update Economics button (Fig. 7) executes amacro which converts the data to a monthly basis and thenprepares an annual economic summary. On the economicsummary worksheet, the user may change net revenue interest(NRI), gas price, well cost, monthly operating cost, and anescalator that is applied to both gas price and operating cost.An example is presented in the next section.

Application of the SoftwareWe will demonstrate the use of the spreadsheet program usingproduction data from Antrim shale and New Albany shaleprojects.

The first example (Fig. 8) is an Antrim shale project inwhich the average well production rate increased to about 260Mscf/D after two years. After 6 years of production, theaverage well gas and water production rates were 125 Mcf/Dand 21 bbls/day, respectively. The parameters selected give agood match of both gas and water production after three years.Fig. 9 gives the cumulative production match and shows anestimated 30-year recovery of about 700 MMscf. Fig. 10gives the economic summary for this average well. For theeconomic parameters specified, the net present value at adiscount rate of 10 percent (NPV10) is $237,000.

The second example (Fig. 11) is a group of New Albanyshale wells for which the initial gas production is about 35

Mscf/D. After 10 years, gas production has declined and isfairly flat at 15 Mscf/D. These wells produced very littlewater. Hence the match value of initial water saturation wasset at the minimum value of 30%. The effective gaspermeability and natural fracture spacing used to match thedata were 0.02 md and 20 ft, respectively. Fig. 12 shows thecumulative production match and forecasts a 30-year recoveryof about 160 MMscf.

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SummaryThe spreadsheet program described in this paper provides apractical engineering method for analyzing production datafrom shale reservoirs. It is simulator based but does notrequire a reservoir simulator. The program may be used tohistory match existing data and/or forecast 30-year production

for a specific set of reservoir properties. An economicsummary worksheet that is also provided may be used as ascreening tool for quickly evaluating the potential of newwells or projects.

We have used spreadsheet programs for the Antrim Shale,New Albany Shale, and New York Devonian Shale. Eachgeologic setting requires a specially developed spreadsheet.Hence, this is not a substitute for simulation. However, ageologic-specific spreadsheet program can be extremely usefulfor both history matching and estimating future production fora wide range of reservoir properties.

Acknowledgement

The authors wish to thank the GRI management and membercompanies and the New York State Energy Research &Development Authority for their contribution and support of this work. We also wish to thank Mr. George W. Voneiff of MGV Energy, Inc. (formerly with S. A. Holditch &Associates) for his contribution to the original development of the menus and dialog boxes used in this spreadsheetapplication.

References1. Chester, Thomas: “Mastering Excel 5 for Windows,”

SYBEX, Inc. Alameda, CA, 1995.2. Shalegas™, A General Purpose Three-Dimensional,

Three-Phase Petroleum Reservoir Simulator, S. A.Holditch & Associates, Inc. User’s Manual, Version 3.0,March 1992.

3. Zuber, M. D., et al.: “Reservoir Characterization andProduction Forecasting for Antrim Shale Wells: AnIntegrated Reservoir Analysis Methodology,” paper SPE28606 presented at the 1994 SPE Annual TechnicalConference and Exhibition, New Orleans, 25-28September.

4. Zuber, M. D., et al.: “Characterization of MichiganAntrim Shale Reservoirs Based on Analysis of Field-Level Data,” paper SPE 29169 presented at the 1994Eastern Regional Conference and Exhibition, Charleston,

WV, 8-10 November.

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Variable Simulation Parameters Currently Selected

a1137 4 3 550 80 1 0.01 9

Simulation File Namesand Input Parametersfor all runsin thisDatabase

FILE Lf Kf Km Fs Pi Pay Area Swi FBHP Phim Phif VL PL TOC Hyd Frac Cond)(ft) (md) (md) (ft) (psia) (ft) (acres) (frac) (psia) (frac) (frac) (scf/ton) (psia) (%) (md-ft)

a0001 100 1 1.00E-08 1 250 100 40 1 30 0.045 0.001 197 1200 9 1000

a0002 100 1 1.00E-08 1 250 100 40 1 30 0.045 0.003 197 1200 9 1000

a0003 100 1 1.00E-08 1 250 100 40 1 30 0.045 0.01 197 1200 9 1000

a0004 100 1 1.00E-08 1 250 100 40 0.8 30 0.045 0.001 197 1200 9 1000

a0005 100 1 1.00E-08 1 250 100 40 0.8 30 0.045 0.003 197 1200 9 1000

a0006 100 1 1.00E-08 1 250 100 40 0.8 30 0.045 0.01 197 1200 9 1000

a0007 100 1 1.00E-08 1 250 100 80 1 30 0.045 0.001 197 1200 9 1000

a0008 100 1 1.00E-08 1 250 100 80 1 30 0.045 0.003 197 1200 9 1000

a0009 100 1 1.00E-08 1 250 100 80 1 30 0.045 0.01 197 1200 9 1000

Fig. 5 – Worksheet showing parameters for the first nine simulations.

A c t u a l P r o d u c t io n (U p t o 5 0 0 R o w s o f D a t a )

C u m G a s G a s W a te r W a te r

T i m e R a te C u m R a te C u m

(D a y s) (M sc f/ d ) (M sc f) (S T B / d ) (S T B )

6 0 . 8 1 6 . 5 5 2 1 1 8 5 . 7 6 , 0 4 5

9 1 . 2 3 0 . 2 1 , 4 3 9 2 1 9 . 5 1 2 , 7 1 71 2 1 . 6 4 8 . 4 2 , 9 1 0 2 2 8 . 3 1 9 , 6 5 8

1 5 2 7 7 . 2 5 , 2 5 8 2 4 2 . 5 2 7 , 0 3 1

1 8 2 . 4 8 3 . 6 7 , 7 9 8 2 0 5 . 2 3 3 , 2 6 9

2 1 2 . 8 1 1 8 . 1 1 1 , 3 8 8 2 2 1 . 7 4 0 , 0 0 8

2 4 3 . 2 1 1 0 . 7 1 4 , 7 5 4 1 9 2 . 9 4 5 , 8 7 3

2 7 3 . 6 1 2 1 . 6 1 8 , 4 5 2 1 9 1 . 6 5 1 , 6 9 8

3 0 4 8 7 . 4 2 1 , 1 1 0 1 4 4 . 6 5 6 , 0 9 3

3 3 4 . 4 1 3 3 . 5 2 5 , 1 6 8 1 5 5 . 6 6 0 , 8 2 4

3 6 4 . 8 1 4 5 . 6 2 9 , 5 9 5 1 8 4 . 8 6 6 , 4 4 1

3 9 5 . 2 1 8 9 . 2 3 5 , 3 4 5 2 2 2 . 8 7 3 , 2 1 5

4 2 5 . 6 2 1 2 . 9 4 1 , 8 1 6 2 0 2 . 9 7 9 , 3 8 4

4 5 6 2 2 1 . 8 4 8 , 5 6 0 1 7 1 . 3 8 4 , 5 9 2

4 8 6 . 4 2 2 1 . 9 5 5 , 3 0 7 1 5 7 . 9 8 9 , 3 9 15 1 6 . 8 2 0 8 . 6 6 1 , 6 4 8 1 4 0 . 2 9 3 , 6 5 3

5 4 7 . 2 2 1 3 . 5 6 8 , 1 3 7 1 4 0 . 0 9 7 , 9 1 1

5 7 7 . 6 2 3 0 . 0 7 5 , 1 3 0 9 3 . 9 1 0 0 , 7 6 6

6 0 8 2 2 6 . 0 8 2 , 0 0 1 9 0 . 9 1 0 3 , 5 2 9

6 3 8 . 4 1 9 7 . 7 8 8 , 0 1 2 9 3 . 0 1 0 6 , 3 5 6

6 6 8 . 8 2 2 4 . 7 9 4 , 8 4 2 8 6 . 9 1 0 8 , 9 9 9

6 9 9 . 2 2 4 7 . 7 1 0 2 , 3 7 3 1 0 7 . 9 1 1 2 , 2 7 9

7 2 9 . 6 2 6 3 . 5 1 1 0 , 3 8 3 1 6 7 . 5 1 1 7 , 3 7 2

C l e a r T h e A c tu a l

P r o d u c ti o n D a ta

Fig. 6 – Observed data worksheet.

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S i m ul a tion -Ba s e d S h a l e Ty pe Cur v e s

S e l e c t y o u r r e s e r v o i r p r o p e r ti e s h e r e Are a P i S w i P h if K f F s T O C

(a cre s) (p sia ) (%) (fra c) (m d ) (ft) (%)

1 2 0 f e e t

Se le c t Your Gr a ph ing Opt ions H e r e

E x t r a T e x t o n G r a p h T i t le : E x a m p l e N o . 1 ( A n t ri m S h a l e )

Ite m T o G r a p h :

G a s Produc t ion Ra t e

E x a m p l e N o . 1 ( A n tr i m S h a l e )

1

10

10 0

1,000

0.0 2 .0 4.0 6.0 8.0 1 0.0

T i m e ( Y e a r s )

G a s R a t e

( M s c f / d

A c t u a l

Simulated

W a t e r P r o d u c t io n R a t e


1

10

10 0

1,000

0.0 2.0 4 .0 6.0 8.0 10.0


W a t e r R a

t e

( B B L /

A c t u a l

Simulated

N e t P a y :

E x p o r t S im u l a t e d D a t a

P r i n t T h i s P a g e

U P D A T E

E C O N O M I C S

Fig. 7 – Main worksheet for selecting parameters for history matching.

S i m u la t io n - B a s e d S h a l e T y p e C u r v e s S e l e c t y o u r r e s e r v o i r p r o p e r t i e s h e r e

A r e a P i S w i P h i f K f F s T O C

(a c r e s) (p si a ) (% ) ( fr a c ) (m d ) (ft ) (% )

1 0 5 f e e t

S e l e c t Y o u r G r a p h i n g O p t io n s H e r e

E x t r a T e x t o n G r a p h T i t le : E x a m p l e N o . 1 ( A n t r im S h a l e )

Ite m T o G r a p h :

G a s P r o d u c t i o n R a t e

E x a m p l e N o . 1 ( A n t r im S h a l e )

1

10

10 0

1 , 0 0 0

0 .0 2 .0 4 .0 6 .0 8 .0 1 0 .0


G a s

R a t e

( M

s c f

A c t u a l

S imu la te d

W a t e r P r o d u c t i o n R a t e

E x a m p l e N o . 1 ( A n t r im S h a l e )

1

10

10 0

1 , 0 0 0

0 .0 2 .0 4 .0 6 .0 8 .0 1 0 .0


W

a t e r R a t e

( B B L

A c t u a l

S imu la te d

N e t P a y :

E x p o r t S i m u l a t e d D a t a


U P D A T E

E C O N O M I C S

Fig. 8 – Example No. 1 – history match of production rates.

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S i m u l a t io n - B a s e d S h a le T y p e C u r v e s

S e l e c t y o u r r e s e r v o i r p r o p e r t i e s h e r e A re a P i S w i P h i f K f F s T O C

(a c re s) (p sia ) (%) (fra c ) (m d ) (ft) (%)

1 0 5 f e e t

S e l e c t Y o u r G ra p h i n g O p t io n s H e re

E x t r a T e x t o n G r a p h T i tle : E x a m p l e N o . 1 ( A n tr im S h a l e )

I te m T o G r a p h :

G a s C u m u l a t i v e P r o d u c t io n


1

10

10 0

1, 000

10, 000

1 0 0 , 0 0 0

1, 000, 000

0 .0 2 .0 4 .0 6 .0 8 .0 1 0 .0


G a s

C u m

( M

s c

A c t u a l

S imulat ed

W a t e r C u m u l a t i v e P r o d u c t i o n

E x a m p l e N o . 1 ( A n t ri m S h a l e )

1

10

10 0

1, 000

10, 000

1 0 0 , 0 0 0

1, 000, 000

0 .0 2 .0 4 .0 6 .0 8 .0 1 0.0


W

a t e r C u m

( B B A c t u a l

S imulat ed

N e t P a y :

E x p o r t S i m u l a t e d D a t a


U P D A T E

E C O N O M I C S

Fig. 9 – Example No. 1 – history match of cumulative production.

E n t e r E c o n o m i c P a r a m e t e rs

N R I 0 .8 7 5 0 0 0 W e ll C o s t $ 2 0 0 ,0 0 0

G a s P ric e 2 .50 0 O p C o s t/m o $ 5 0 0 N P V 1 0 = $ 2 3 7 ,0 5 6

E s ca la to r 1 .00 5 F irs t Ye a r 2 0 0 0

G ro ss N e t D isc o u n te d C u m u la tiv eG a s G a s G a s O p e ra tin g C u m u la tive N e t Re v e n u e D isc o u n te d

YE A R M sc f M scf P ric e C o st N e t Re v e n u e N e t Re ve n u e 1 0 % N e t Re ve n u e

1 2000 28706 25118 2 .500 6000 -143205 -143205 -130186 -330186

2 2001 41486 36300 2 .513 6 ,030 85174 -58031 70392 -259794

3 2002 54637 47808 2 .525 6 ,060 114657 56626 86143 -173651

4 2003 51211 44810 2 .538 6 ,090 107623 164250 73508 -100143

5 2004 46341 40549 2 .550 6 ,121 97293 261543 60411 -39731

6 2005 39771 34800 2 .563 6 ,152 83044 344587 46876 7145

7 2006 34471 30162 2 .576 6 ,182 71514 416101 36698 43843

8 2007 30613 26786 2 .589 6 ,213 63131 479232 29451 73294

9 2008 27699 24237 2 .602 6 ,244 56814 536046 24094 97389

10 2009 25452 22271 2 .615 6 ,275 51957 588003 20032 117421

11 2010 23705 20742 2 .628 6 ,307 48201 636204 16894 134315

12 2011 22280 19495 2 .641 6 ,338 45147 681351 14385 148700

13 2012 21004 18379 2 .654 6 ,370 42410 723761 12285 160985

14 2013 20051 17544 2 .667 6 ,402 40397 764158 10638 171622

15 2014 19137 16744 2 .681 6 ,434 38455 802612 9206 180828

16 2015 18326 16036 2 .694 6 ,466 36737 839350 7995 188823

17 2016 17610 15409 2 .708 6 ,498 35224 874574 6969 195792

18 2017 16965 14844 2 .721 6 ,531 33863 908437 6091 201883

19 2018 16379 14331 2 .735 6 ,564 32630 941067 5335 207218

20 2019 15845 13864 2 .748 6 ,596 31510 972577 4684 211902

21 2020 15358 13438 2 .762 6 ,629 30490 1003066 4120 21602222 2021 14908 13045 2 .776 6 ,663 29551 1032617 3630 219652

23 2022 14495 12683 2 .790 6 ,696 28688 1061305 3204 222856

24 2023 14110 12347 2 .804 6 ,729 27889 1089194 2831 225687

25 2024 13750 12031 2 .818 6 ,763 27140 1116335 2505 228192

26 2025 13415 11738 2 .832 6 ,797 26446 1142781 2219 230411

27 2026 13101 11464 2 .846 6 ,831 25796 1168577 1968 232379

28 2027 12806 11205 2 .860 6 ,865 25187 1193763 1747 234125

29 2028 12526 10960 2 .875 6 ,899 24607 1218371 1551 235676

30 2029 12264 10731 2 .889 6 ,934 24069 1242440 1379 237056

708423 619870 1242440

Fig. 10 – Example No. 1 – economic summary.

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S i m u l a ti o n -B a s e d S h a l e T y p e C u r v e s

S e l e c t y o u r r e s e r v o i r p r o p e r t ie s h e r e A re a P i S w i P h i f K f F s T O C

(a c re s) (p si a ) (%) (fra c) (m d ) (ft) (%)

8 6 f e e t

S e l e c t Y o u r Gr a p h i n g Op t io n s H e r e

E x t r a T e x t o n G r a p h T i tl e : E x a m p l e N o . 2 ( N e w A l b a n y S h a l e )

I te m T o G r a p h :

G a s P r o d u c t i o n R a t e

E x a m p l e N o . 2 ( N e w A l b a n y S h a l e )

0

5

10

15

20

25

30

35

40

0 .0 5 .0 10.0 1 5.0 2 0.0 2 5.0 30 .0


G a s

R a t e

( M

s c f /

A c t u a l

Simulated

W a t e r P r o d u c t io n R a t e

E x a m p l e N o . 2 ( N e w A l b a n y S h a l e )

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0 .0 2 .0 4 .0 6 .0 8 .0 1 0.0 1 2.0 14.0


W

a t e r R a t e

( B B L /

A c t u a l

Simulated

N e t P a y :

E x p o r t S im u l a t e d D a t a


U P D A T E

E C O N O M I C S

Fig. 11 – Example No. 2 – history match of gas production rate.

G a s C u m u l a t i ve P ro d u c t i o n

Ex a m p l e N o . 2 (N e w A l b a n y S h a l e )

0

2 0 , 0 0 0

4 0 , 0 0 0

6 0 , 0 0 0

8 0 , 0 0 0

1 0 0 , 0 0 0

1 2 0 , 0 0 0

1 4 0 , 0 0 0

1 6 0 , 0 0 0

1 8 0 , 0 0 0

0 .0 5 .0 1 0 .0 1 5 .0 2 0 .0 2 5 .0 3 0 .0

T im e ( Y e a r s )

G a s

C u m

( M

s

A c t u a l

S imula ted

Fig. 12 – Example No. 2 – history match of cumulative gas production.

Spe 57439

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