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, 2008

Denver, CO

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COGA_AAPG SP070908

Abstract

Estimating potential hydrocarbon recoveries from greenfield (but resource-rich) unconventional gas plays is a challenge. While analogs are routinely employed for this purpose, there is no shortage ofexamples of how frontier tight sand, coalbed methane and organic shale plays have required new operating practices that can run contrary to historical (analog) experience. An analytic approach is presented to account for whatever limited geologic and reservoir information might be available for a new resource play, and based upon sound engineering principles, make predictions of potential gas recoveries, their variability, and identify areas of uncertainty. The methodology involves selecting the potential ranges (and distributions where appropriate) of reservoir parameters across a particular acreage position, such as depth and pressure, formation thickness, porosity, fluid saturations, permeability, relative permeability, etc. Where no data exists, analogs and experience must still be employed. Single-well probabilistic reservoir simulation forecasting is then performed using Monte Carlo methods to establish a distribution of potential well recoveries, which are in turn used for field development planning and economic analysis. Factors having the greatest impact on well recoveries and economics can be identified via statistical analysis of the results, thus focusing field data collection efforts 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 isto drill and produce commercial wells.”

- Anonymous

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Resource Classification System and Project Maturity Sub-Classes

Source: SPE Petroleum Resources Management System, 2007

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CBM Reserves Assignment Methodology

Source: SPEE (Calgary Chapter), Canadian Oil & Gas Evaluation Handbook, Volume 3: Detailed Guidelines for Estimation and Classification of CBM Reserves and Resources, First Edition, June 10, 2007.

Proved

Probable

Possible

Contingent

Prospective

Key

Commercially Producing Well (Different scheme if a data well)

Drilling Spacing Unit (DSU)

X

9 proved16 probable24 possible

120 contingent

1 well =

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Example Application

0255075

100125150175200225250275300325350375400425450475500525550575600625

0 10 20 30 40 50 60 70 80

Number of Commercially Producing Wells

# DSU

's

1P2P3PContingentProspective

Assumptions100,000 acres160 acre DSU625 DSU total

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Objective of Presentation

• How does an operator assess “likely” reserves and commerciality in the early stages of play development?

• 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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A Few Premises

• Unconventional gas plays are statistical – many wells drilled with a manufacturing mentality yielding a distribution of outcomes.– Why? - presumably reservoir heterogeneity.

• “Best” drilling, completion, stimulation & production practices evolve over time.– i.e., early production results are likely understated.

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The Statistical Nature of Unconventional Gas – A Coalbed Methane Example

By Basin By Field in a Basin

By Well in a Field

Source: SPE 98069; “Challenging the Traditional Coalbed Methane Exploration and Evaluation Model”, Weida, S. D., Lambert, S. W., and Boyer II, C. M., 2005.

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Evolution of “Best” Practices - The Barnett Shale

Source: Pickering Energy Partners, The Barnett Shale – Visitors Guide to the Hottest Gas Play in the U.S., October, 2005.

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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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Modeling Permeability Variability Using Geostatistics

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

Var

ianc

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 ContinuityRotated X axis (E60S)

γ(h) = 1.5 h

1,200 - 0.5 h1,200

3

, if h ≤ 1,200

1 , otherwiseγ(h) =

1.5 h1,2001.5 h1,200

h1,200 - 0.5 h

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

NuggetEffect Range

Var

ianc

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 RangeRange

Var

ianc

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 ContinuityRotated X axis (E60S)

γ(h) = 1.5 h

1,200 - 0.5 h1,200

3

, if h ≤ 1,200

1 , otherwiseγ(h) =

1.5 h1,2001.5 h1,200

h1,200 - 0.5 h

1,200

3

- 0.5 h1,200

3h

1,200h

1,200

3

, if h ≤ 1,200

1 , otherwise

Spherical Variogram Model

γ(h) = 1.5 h

1,200 - 0.5 h1,200

3

, if h ≤ 1,200

1 , otherwiseγ(h) =

1.5 h1,2001.5 h1,200

h1,200 - 0.5 h

1,200

3

- 0.5 h1,200

3h

1,200h

1,200

3

, if h ≤ 1,200

1 , otherwise

Spherical Variogram Model

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Permeability – Porosity Relationships

3

00

kk

⎟⎟⎠

⎞⎜⎜⎝

⎛=

φφ

3 k A =φ

εφ k A 3 +=

ε is an added random error!

3

00

kk

⎟⎟⎠

⎞⎜⎜⎝

⎛=

φφ

3 k A =φ

3

00

kk

⎟⎟⎠

⎞⎜⎜⎝

⎛=

φφ

3 k A =φ

εφ k A 3 += εφ k A 3 +=

ε 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

Poro

sity

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The Importance of Accounting for Permeability/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

Average Permeability

Cum

. Tot

al G

as

Permeability heterogeneously distributed

Permeability homogeneously 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

Average Permeability

Cum

. Tot

al G

as

Permeability heterogeneously distributed

Permeability 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

Cum

ulat

ive

Tota

l Gas

(Msc

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

Cum

ulat

ive

Tota

l Gas

(Msc

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

Cum

ulat

ive

Tota

l Gas

(Msc

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

Cum

ulat

ive

Tota

l Gas

(Msc

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

Cum

ulat

ive

Tota

l Gas

(Msc

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

Cum

ulat

ive

Tota

l Gas

(Msc

f)

Porosity heterogeneously distributed

Porosity homogeneously distributed

Lognormal Distribution

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0 100 200 300 400 500Permeability (mD)

Freq

uenc

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 500Permeability (mD)

Freq

uenc

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

Freq

uenc

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

Freq

uenc

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

Freq

uenc

y

Mean = 0.05Truncated!

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Relative Permeability

m

rw SgrSwrSwrSwKKrw ⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

−=

1max

n

rg SgrSwrSgrSwKKrg ⎟⎟

⎠

⎞⎜⎜⎝

⎛−−−−

=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

Kr

Krw Krg

Swr=0.1 Sgr=0.2

Krwmax=0.9Krgmax=0.7

m=4.5n=1.5

Uniform(0.2, 0.5)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

90.0%0.2150 0.4850

m

rw SgrSwrSwrSwKKrw ⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

−=

1max

n

rg SgrSwrSgrSwKKrg ⎟⎟

⎠

⎞⎜⎜⎝

⎛−−−−

=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

Kr

Krw Krg

Swr=0.1 Sgr=0.2

Krwmax=0.9Krgmax=0.7

m=4.5n=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

Kr

Krw Krg

Swr=0.1 Sgr=0.2

Krwmax=0.9Krgmax=0.7

m=4.5n=1.5

Uniform(0.2, 0.5)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

90.0%0.2150 0.4850

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Building the Model

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

Well 1

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

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

Well 1

• Single – well• Mutiple layer (can use clustering methods toestablish layering scheme)

• Probablistic distributions of reservoir properties

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Establishing Layering Scheme Using Clustering

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

JALEEL-1H2

JALEEL 1H2

14750 ft

14875 ft

15000 ft

15125 ft

GR (Raw)(GAPI) 0 200

DEN (Raw)

(G/CM3) 2.2 2.8

JALEEL-1H2

JALEEL 1H2

14750 ft

14875 ft

15000 ft

15125 ft

GR (Raw)(GAPI) 0 200

DEN (Raw)

(G/CM3) 2.2 2.8

JALEEL-1H2

14750 ft

14875 ft

15000 ft

15125 ft

GR (Raw)(GAPI) 0 200

DEN (Raw)

(G/CM3) 2.2 2.8

JALEEL-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 software (GAMLS) permits clustering using mixed variables, and probabilistic assignment of samples to each multi dimensional cluster.

At each depth, clustering analysis provides rock types with different RQ, and estimates of an analyzed reservoir parameter.

Clustering methods are applied to selected well logs and core data for lithology interpretation, and reservoir quality (RQ) characterization.

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

Colorful Track: Probabilistic representation of clusters

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Establishing Probabilistic Variables

Parameter 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)

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Sample Outcome

0

5

1015

20

25

30

3540

45

50

0

120 240 360 480 600 720 840 960108

0120

0

Cumulative Gas, MMcf

Freq

uenc

y

0%

10%

20%30%

40%

50%

60%

70%80%

90%

100%

Cum

ulat

ive

Perc

enta

geAverage= 513 MMcf

Dry Holes

Each case has unique production profile!

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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 Anisotrophy Fac

Initial Water Sat

Irred Water Sat

Skin Prod

KrwExp

KrgMax

Frac Spacing

Water Density

Lang Vol

Pd_Pi

Sorption Time

KrgExp

Por Frac

Avg Permeability

Rank Correlation

ImportantImportant Questionable

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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

Prod

uctio

n R

ate

-

50

100

150

200

250

300

350

Wel

l Cou

nt, C

umul

ativ

e Pr

oduc

tion

Gas Rate,mcf/dWater Rate,bbl/dCum Gas, bcfWell CountCum Water, MMbbl

•Set constraints (e.g., drilling rate, max gas production, etc.)

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COGA_AAPG SP070908

Final Remarks

• Integrated reservoir and Monte Carlo simulation approach replicates statistical nature of unconventional gas plays while honoring physical nature of complex reservoir system.

• Results provide a range of outcomes that can be used for more realistic development planning and economic analysis.

• Sensitivity analysis can be used to focus data collection efforts where they can have the greatest impact on uncertainty reduction.

• Accounting for permeability and porosity variability has important implications for production forecasting.

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Office Locations

Washington, DC4501 Fairfax Drive, Suite 910Arlington, 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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