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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COGA_AAPG SP070908
Resource Classification System and Project Maturity Sub-Classes
Source: SPE Petroleum Resources Management System, 2007
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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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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 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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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COGA_AAPG SP070908
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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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 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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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
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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COGA_AAPG SP070908
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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