rocky mountain g & e resources conference

7/27/2019 Rocky Mountain G & E Resources Conference

1/23

Unconventional Gas Resources to Reserves A Predictive Approach

Unconventional Gas Resources to Reserves A Predictive Approach

Presented By:Scott Reeves

ADVANCED RESOURCES INTERNATIONAL, INC.Houston, TX

Rocky Mountain Geology & Energy Resources ConferenceJuly 9 - 11, 2008Denver, CO


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COGA_AAPG SP070908

AbstractEstimating potential hydrocarbon recoveries from greenfield (butresource-rich) unconventional gas plays is a challenge. While analogsare routinely employed for this purpose, there is no shortage of examples of how frontier tight sand, coalbed methane and organic shaleplays have required new operating practices that can run contrary tohistorical (analog) experience. An analytic approach is presented toaccount for whatever limited geologic and reservoir information might beavailable for a new resource play, and based upon sound engineeringprinciples, make predictions of potential gas recoveries, their variability,and identify areas of uncertainty. The methodology involves selectingthe potential ranges (and distributions where appropriate) of reservoirparameters across a particular acreage position, such as depth andpressure, formation thickness, porosity, fluid saturations, permeability,relative permeability, etc. Where no data exists, analogs and experiencemust still be employed. Single-well probabilistic reservoir simulationforecasting is then performed using Monte Carlo methods to establish adistribution of potential well recoveries, which are in turn used for fielddevelopment planning and economic analysis. Factors having thegreatest impact on well recoveries and economics can be identified viastatistical analysis of the results, thus focusing field data collectionefforts on issues with the greatest potential for uncertainty reduction.


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COGA_AAPG SP070908

Resources-to-Reserves: The Short,Practical Answer

The only way to convert resources to reserves is

to drill and produce commercial wells.

- Anonymous


4/23


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COGA_AAPG SP070908

CBM Reserves Assignment Methodology

Source: SPEE (Calgary Chapter), Canadian Oil & Gas Evaluation Handbook, Volume 3:Detailed Guidelines fo r Estimation and Classifi cation of CBM Reserves and Resources,

First Edition, June 10, 2007.

Proved

Probable

Possible

Contingent

Prospective

Key

Commercially ProducingWell (Different scheme if adata well)

Drilling SpacingUnit (DSU)

X

9 proved16 probable24 possible

120 contingent

1 well =


6/23

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COGA_AAPG SP070908

Example Application

0255075

100125150175200225250275300325

350375400425450475500525550575600625

0 10 20 30 40 50 60 70 80

Number of Commercially Producing Wells

#

D S U ' s

1P

2P3PContingentProspective

Assumptions100,000 acres160 acre DSU625 DSU total


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COGA_AAPG SP070908

Objective of Presentation

How does an operator assess likelyreserves

and commerciality in the early stages of playdevelopment? At the point in time when a resource

assessment has been performed, and (limited)well/production data available.

i.e., The production forecasting challenge.


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COGA_AAPG SP070908

A Few Premises

Unconventional gas plays are statistical many

wells drilled with a manufacturing mentalityyielding a distribution of outcomes. Why? - presumably reservoir heterogeneity.

Bestdrilling, completion, stimulation &production practices evolve over time. i.e., early production results are likely understated.


9/23

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COGA_AAPG SP070908

The Statistical Nature of UnconventionalGas A Coalbed Methane Example

By Basin By Field in a Basin

By Well in a Field

Source: SPE 98069; Challengingthe Traditional Coalbed MethaneExploration and Evaluation Model ,Weida, S. D., Lambert, S. W., and

Boyer II, C. M., 2005.


10/23


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COGA_AAPG SP070908

The Production Forecasting Approach Unconventional gas reservoir simulation

to account for complex reservoir behavior

Monte-Carlo simulation to account for statistical variability of reservoir

properties

Plus... experience analogy skepticism

etc.


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COGA_AAPG SP070908

Modeling Permeability Variability UsingGeostatistics

Sill

0.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 18000.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 1800

NuggetEffect Range

V a r i a n c e

Y axis(North)

Range = 1,200 m

Minor range = 600 m

Anisotropy Coefficient = 1/2Major Direction of ContinuityRotated Y axis (N60E)

X axis(East)

Azimuth = 60

Minor Direction of Continuity

Rotated X axis (E60S)

(h) =1.5 h1,200 - 0.5

h1,200

3

, if h 1,200

1 , otherwise(h) =

1.5 h1,2001.5h

1,200h

1,200 - 0.5h

1,200

3

- 0.5 h1,200

3h

1,200h

1,200

3

, if h 1,200

1 , otherwise

Spherical Variogram Model

Sill

0.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 18000.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 1800

Nugge tEffect Range

V a r i a n c e

Sill

0.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 18000.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 18000.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 18000.00

0.25

0.50

0.75

1.00

0 200 400 600 800 1000 1200 1400 1600 1800

NuggetEffect Rang eRang e

V a r i a n c e

Y axis(North)

Range = 1,200 m

Minor range = 600 m

Anisotropy Coefficient = 1/2Major Direction of ContinuityRotated Y axis (N60E)

X axis(East)

Azimuth = 60

Minor Direction of Continuity

Rotated X axis (E60S)

(h) =1.5 h1,200 - 0.5

h1,200

3

, if h 1,200

1 , otherwise(h) =

1.5 h1,2001.5h

1,200h

1,200 - 0.5h

1,200

3

- 0.5 h1,200

3h

1,200h

1,200

3

, if h 1,200

1 , otherwise


(h) =1.5 h1,200 - 0.5

h1,200

3

, if h 1,200

1 , otherwise(h) =

1.5 h1,2001.5h

1,200h

1,200 - 0.5h

1,200

3

- 0.5 h1,200

3h

1,200h

1,200

3

, if h 1,200

1 , otherwise



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COGA_AAPG SP070908

Permeability Porosity Relationships

3

00

k

k

=

3 kA =

kA 3 +=

is an added random error!

3

00

k

k

=

3 kA =

3

00

k

k

=

3 kA =

kA 3 += kA 3 +=

is an added random error!

0

0.05

0.1

0.15

0.2

0.25

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

Permeability

P o r o s i

t y


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COGA_AAPG SP070908

The Importance of Accounting forPermeability/Porosity Heterogeniety

0

200000

400000

600000

800000

1000000

1200000

0 2.5 5 7.5 10 12.5 15 17.5 20 22.5 25 27.5

Av erage Permeability

C u m . T

o t a l G a s

Permeability heterogeneously distributed

Permeabilit y homo geneously distributed

0

200000

400000

600000

800000

1000000

1200000

0 2.5 5 7.5 10 12.5 15 17.5 20 22.5 25 27.5

Av erage Permeability

C u m . T

o t a l G a s

Permeability heterogeneously distributed

Permeabilit y homo geneously distributed

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

0.000 0. 001 0. 002 0. 003 0. 004 0. 005 0. 006 0. 007 0. 008 0. 009 0. 010 0.011 0. 012 0. 013 0. 014

Fracture Porosity

C u m u

l a t i v e

T o

t a l G a s

( M s c

f )

Porosity heterogeneously distributed

Porosity homogeneously distributed

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

0.000 0. 001 0. 002 0. 003 0. 004 0. 005 0. 006 0. 007 0. 008 0. 009 0. 010 0.011 0. 012 0. 013 0. 014

Fracture Porosity

C u m u

l a t i v e

T o

t a l G a s

( M s c

f )

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

0.000 0. 001 0. 002 0. 003 0. 004 0. 005 0. 006 0. 007 0. 008 0. 009 0. 010 0.011 0. 012 0. 013 0. 014

Fracture Porosity

C u m u

l a t i v e

T o

t a l G a s

( M s c

f )



0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

0.000 0. 001 0. 002 0. 003 0. 004 0. 005 0. 006 0. 007 0. 008 0. 009 0. 010 0.011 0. 012 0. 013 0. 014

Fracture Porosity

C u m u

l a t i v e

T o

t a l G a s

( M s c

f )



0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

0.000 0. 001 0. 002 0. 003 0. 004 0. 005 0. 006 0. 007 0. 008 0. 009 0. 010 0.011 0. 012 0. 013 0. 014

Fracture Porosity

C u m u

l a t i v e

T o

t a l G a s

( M s c

f )

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

0.000 0. 001 0. 002 0. 003 0. 004 0. 005 0. 006 0. 007 0. 008 0. 009 0. 010 0.011 0. 012 0. 013 0. 014

Fracture Porosity

C u m u

l a t i v e

T o

t a l G a s

( M s c

f )



Lognormal Distribution

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0 100 200 300 400 500

Permeability (mD)

F r e q u e n c y

Mean = 100 mD

Lognormal Distribution

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0 100 200 300 400 500

Permeability (mD)

F r e q u e n c y

Mean = 100 mD

Normal Distribution

0

5

10

15

20

25

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12Porosity

F r e q u e n c y

Mean = 0.05Truncated!

Normal Distribution

0

5

10

15

20

25

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12Porosity

F r e q u e n c y

Mean = 0.05

Normal Distribution

0

5

10

15

20

25

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12Porosity

F r e q u e n c y

Mean = 0.05Truncated!


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COGA_AAPG SP070908

Relative Permeabilitym

rw Sgr Swr

Swr SwK Krw

=1max

n

rgSgr Swr

Sgr SwK Krg

=

11

max

0.5 0.6 0.8 0.9 1.0

CoreyCstKrg

1.5 2.3 3.0 3.8 4.5

CoreyExpKrg

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Sw

K r

Krw Krg

Swr=0.1 Sgr=0.2

Krwmax=0.9

Krgmax=0.7

m=4.5

n=1.5

Uniform(0.2, 0.5)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 . 1

5

0 . 2

0

0 . 2

5

0 . 3

0

0 . 3

5

0 . 4

0

0 . 4

5

0 . 5

0

0 . 5

5

90.0%0.2150 0.4850

m

rw Sgr Swr

Swr SwK Krw

=1max

n

rgSgr Swr

Sgr SwK Krg

=

11

max

0.5 0.6 0.8 0.9 1.0

CoreyCstKrg

1.5 2.3 3.0 3.8 4.5

CoreyExpKrg

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Sw

K r

Krw Krg

Swr=0.1 Sgr=0.2

Krwmax=0.9

Krgmax=0.7

m=4.5

n=1.5

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Sw

K r

Krw Krg

Swr=0.1 Sgr=0.2

Krwmax=0.9

Krgmax=0.7

m=4.5

n=1.5

Uniform(0.2, 0.5)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 . 1

5

0 . 2

0

0 . 2

5

0 . 3

0

0 . 3

5

0 . 4

0

0 . 4

5

0 . 5

0

0 . 5

5

90.0%0.2150 0.4850


16/23

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COGA_AAPG SP070908

Building the Model

X-Dir. Perm. in Fract., md7.7082 1133.3800570.5441289.1262 851.9620

Well 1



Well 1

Single well Mutiple layer (can use clustering methods to

establish layering scheme) Probablistic di stributions of reservoir properties


17/23

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COGA_AAPG SP070908

Establishing Layering Scheme UsingClustering

MABROUK 4H1

15250 ft

15375 ft

15500 ft

15625 ft

GR (Raw)(GAPI)0 200

DENS (Raw)

(G/C3)1.9 2.8

MABROUK-4H1

MABROUK 4H1

15250 ft

15375 ft

15500 ft

15625 ft

GR (Raw)(GAPI)0 200

DENS (Raw)

(G/C3)1.9 2.8

MABROUK-4H1

MABROUK 4H1

15250 ft

15375 ft

15500 ft

15625 ft

GR (Raw)(GAPI)0 200

DENS (Raw)

(G/C3)1.9 2.8

MABROUK-4H1

15250 ft

15375 ft

15500 ft

15625 ft

GR (Raw)(GAPI)0 200

DENS (Raw)

(G/C3)1.9 2.8

MABROUK-4H1

JALEEL 1H2

14750 ft

14875 ft

15000 ft

15125 ft

GR (Raw)(GAPI)0 200

DEN (Raw)

(G/CM3)2.2 2.8

J ALEEL-1H2

JALEEL 1H2

14750 ft

14875 ft

15000 ft

15125 ft

GR (Raw)(GAPI)0 200

DEN (Raw)

(G/CM3)2.2 2.8

J ALEEL-1H2

JALEEL 1H2

14750 ft

14875 ft

15000 ft

15125 ft

GR (Raw)(GAPI)0 200

DEN (Raw)

(G/CM3)2.2 2.8

J ALEEL-1H2

14750 ft

14875 ft

15000 ft

15125 ft

GR (Raw)(GAPI)0 200

DEN (Raw)

(G/CM3)2.2 2.8

J ALEEL-1H2

M2_Sh3

M1_Sh2

M6_Sh1

M5_Slt2

M3_Slt1

M4_Ss

Different tones of . . . gray : possible shale green : possible siltstone yellow : possible sandstone

A data-driven sof tware (GAMLS) permitsclustering using mixed variables, andprobabilistic assignment of samples to eachmulti dimensional cluster.

At each depth, clus tering analysisprovides rock types with differentRQ, and estimates of an analyzedreservoir parameter.

Clustering methods are applied to selected welllogs and core data for lithology interpretation,

and reservoir quality (RQ) characterization.

Logs Track : Density (red) & GR (blue)

Colorfu l Track : Probabilistic representationof clusters


18/23

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COGA_AAPG SP070908

Establishing Probabilistic VariablesParameter Units Distribution

Initial Water Saturation fraction T(0.7, 1,1)

Average Permeability md L(5,2)

Permeability Anisotropy Factor dimensionless T(1,2,5)

Fracture Porosity fraction N(0.005, 0.002)

Sorption Time days T(30, 300, 3000)

Fracture Spacing inches T(12, 36, 120)

Langmuir Volume cuft/cuft T(3.82, 6.82, 9.82)

Langmuir Pressure psi T(300, 354, 400)

Water Density lb/ft T(43.2, 50.42, 57.63)

Skin Factor dimensionless T(-4, 0, -2)

Desorption Pressure Function dimensionless T(0.7, 1,1)

Irreducible Water Saturation fraction T(0.1, 0.2, 0.3)

Maximum gas relative permeability dimensionless T(0.5, 0.75, 1)

Corey gas exponent dimensionless T(1, 2, 3)

Corey water exponent dimensionless T(1, 2, 3)

Azimuth degrees U(-90, 90)

Nugget Effect dimensionless U(0,1)

Range mts U(800, 2000)

Anisotropy Coefficient dimensionless U(0.001, 1.0)


19/23

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COGA_AAPG SP070908

Sample Outcome

0

5

10

15

20

25

30

35

40

45

50

0 1 2

0 2 4

0 3 6

0 4 8

0 6 0

0 7 2

0 8 4

0 9 6

0 1 0

8 0 1 2

0 0

Cumulative Gas, MMcf

F r e q u e n c y

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

C u m u

l a t i v

e P e r c e n

t a g e

Average= 513 MMcf

Dry Holes

Each case has uniqueproduction profile!


20/23

20

COGA_AAPG SP070908

Understanding Critical Success Factors

Cumulative Total Gas

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Lang Press

Perm Ani sotrophy Fac

Initial Water Sat

Irred Water Sat

Skin Prod

KrwExp

KrgMaxFrac Spacing

Water Density

Lang Vol

Pd_Pi

Sorption Time

KrgExp

Por Frac

Avg Permeabi li ty

Rank Correlation

ImportantImportant Questionable


21/23

21

COGA_AAPG SP070908

Field Development Planning*

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

0 2 4 6 8 10 12 14 16 18 20

Time, years

P r o

d u c t

i o n

R a t e

-

50

100

150

200

250

300

350

W e l

l C o u n

t , C u m u l a

t i v e

P r o

d u c t

i o n

Gas Rate,mcf/dWater Rate,bbl/dCum Gas, bcf Well Count

Cum Water, MMbbl

Set constraints (e.g., drilling rate, max gasproduction, etc.)


22/23

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COGA_AAPG SP070908

Final Remarks

Integrated reservoir and Monte Carlo simulation approachreplicates statistical nature of unconventional gas playswhile honoring physical nature of complex reservoirsystem.

Results provide a range of outcomes that can be used formore realistic development planning and economic

analysis. Sensitivity analysis can be used to focus data collection

efforts where they can have the greatest impact onuncertainty reduction.

Accounting for permeability and porosity variability hasimportant implications for production forecasting.


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COGA_AAPG SP070908

Office Locations

Washington, DC4501 Fairfax Drive, Suite 910

Arlington, VA 22203 USAPhone: (703) 528-8420Fax: (703) 528-0439

Houston, Texas11490 Westheimer Rd., Suite 520Houston, TX 77077 USAPhone: (281) 558-9200Fax: (281) 558-9202

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