rocky mountain g & e resources conference
TRANSCRIPT
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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
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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 =
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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.
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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.
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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
Spherical Variogram Model
(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
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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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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 )
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 )
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 )
Porosity heterogeneously distributed
Porosity homogeneously distributed
Lognormal Distribution
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0 100 200 300 400 500
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
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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 to
establish layering scheme) Probablistic di stributions of reservoir properties
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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
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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)
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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!
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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 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
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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
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.)
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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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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
For More Information...