m - REU Houston 2017

New Methodology for Estimating
Unconventional Production and Long Term
Performance
Ian Walton, Ph.D.
Senior Research Scientist
[email protected]
Reserves Estimation Unconventionals
Houston August 2016
Project I 01024
[email protected]
Unconventional Production
Production from multi-fractured long horizontal
wells
• Ultra-low permeability reservoir
• Most gas resident in the pores
• Presence of a complex network of natural fractures
[email protected]
Outline

Quantifying well productivity
•
•
•
•
•

Short-term
Medium-term
Long-term
Single-phase: gas, oil
Multi-phase: gas/oil
Relating productivity to key parameters
• Reservoir properties
• Completions parameters
• Operational parameters

Estimating long-term recovery from limited
production data
[email protected]
Some Shale Gas Production Data
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
2
4
6
time (years)
1,600
20000
18000
16000
14000
12000
10000
8000
6000
4000
2000
0
water rate
drawdown
gas rate
1,400
1,200
1,000
800
600
400
200
0
0
50
100
150
time (days)
Baihly et al (SPE 135555)
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200
250
gas prodcution (Mscf/day)
0
water production (bbl/day)
production rate (bscf/year)
1.8
Conventional Production Data Analysis Techniques:
Decline Curve Analysis
daily production, Mscf/day
daily production
data
1500
"b=1.6"
"b=2"
1000
500
1500
2
1.5
1000
1
500
0.5
0
0
0
50
100
150
200
time (days)
250
qi
(1  bDi t )
1
for 0  b  1
b
0
0
300
1095 2190 3285 4380 5475 6570 7665 8760
time (days)
Conventional Decline Analysis—Arps
q
2.5
data
"b=1.6"
"b=2"
"b=1.6"
"b=2"
cumulative production, bscf
2000
2000
Advantages
•Quick, easy to use, familiar
Disadvantages
•Limited basis in the physics
•Not appropriate for transient flow
•No insights into production drivers
•Exponent b is assumed to be
constant
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Shale Gas Production Models
Numerical, incorporating flow in
matrix and fracture networks
− discrete fracture network
− continuum model (dual
porosity/dual permeability)
Semi-analytic,
• continuum model (dual
porosity/dual permeability)
• perturbation solution appropriate
for shale gas reservoirs
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Quantifying Productivity: Production Coefficient
Theory suggests that for a substantial period
of time cumulative production and
production rate can be approximated by
Q  C p t,
q
1
2
Cp
t
where C p depends on
–
–
–
–
Pressures (bhfp, pore or reservoir pressure)
Reservoir quality/ GIP (permeability, porosity)
Gas properties (viscosity, compressibility,
equation of state)
Productive fracture surface area
Cp
p
A
2
r
 p w2
ps

c m k m

Tm 
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 2
c m  L
km
2
New Production Data Analysis Method
OLD
NEW
1200000
cumulative production (mscf)
1.8
production rate (bsc/yearf)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
time (years)
•
•
800000
600000
400000
200000
0
0
•
•
•
1000000
0
0.5
1
1.5
sqrt(time) years^0.5
2
2.5
Plot cumulative production against the square-root of time: expect straight line
Slope of the line is the best metric of well productivity: Production Coefficient
Solution is valid for many years of production
Production data analysis is efficient and effective: data from thousands of wells
have been analyzed
Provides a rational basis for evaluating the production drivers, quantifying “what
makes a good well”, assessing play-by-play variations and evaluating the role of
natural fractures.
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Impact of Completions Parameters
160
y = 17.109x - 77.545
cumulative production
140
120
350 ft spacing
100
250 ft spacing
80
y = 11.976x - 40.681
60
40
20
0
0
5
sqrt (time)
10
15
Ratio of production coefficients = 1.42
Cp
p
A
2
r
 p w2
ps

c m k m

Ratio of # fractures = 350/250 = 1.4
Impact on EUR?
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Impact of Variable Drawdown
Granite Wash Data (SPE 163820)
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Early-time Transient Regime
cumulative production
45000
40000
Barnett
35000
Fayettville
30000
Woodford
25000
Haynesville
20000
15000
10000
5000
0
0
1
2
sqrt (time) (sqrt(months))
[email protected]
3
4
water rate
drawdown
gas rate
1,600
1,400
1,200
1,000
800
600
400
200
0
0
50
100
150
200
20000
18000
16000
14000
12000
10000
8000
6000
4000
2000
0
gas prodcution (Mscf/day)
water production (bbl/day)
Identification of Flow Regimes
250
time (days)
Transient
regime I
Transient
regime II
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Boundaryimpacted
regime
Decline rate and Arps exponent
dq
D
dt
q
b
Arps exponent:
Linear Flow regime
Boundary-impacted regime
q
1
2
Cp
t
D   1 dD
dt
D 2 dt
D
Q  Q (1  e
D
d1
 t
)
1
2t
b2
q  Q e
 t
 
b0
Tm 
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2 1
4 Tm
 2
c m  L
km
2
Variation of Arps b-exponent
Transient
regime I
Transient
regime II
Boundaryimpacted regime
Terminal
decline
rate: 12%
b=??
b=2
b=0
[email protected]
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New Estimates of Terminal Decline Rate and EUR
•Terminal decline rate

2
1
D 
4 Tm
Tm 
 2
c m  L
2
km
• From production data, identify point at which
linear flow ends:
Q  Qelf
D  Delf
•From the semi-analytic solution transition to
boundary dominated regime (exponential
decline) begins (linear flow ends) at about
t elf  0.15Tm
Cumulative Oil (bbl)
t  t elf
900000
800000
700000
600000
500000
400000
300000
200000
100000
0
0
2
4
6
8
sqrt (time) (Months After Initial Production)^0.5
[email protected]
New Estimates of Terminal Decline Rate and EUR
D 

2
1
4 Tm
Tm 
 2
c m  L
2
km
t elf  0.15Tm
• New estimate of terminal decline rate
Delf 
1
1
4


D  1.35D
2t elf 0.3Tm 0.3 2
Cumulative Oil (bbl)
•New estimate of EUR
900000
800000
700000
600000
500000
400000
300000
200000
100000
0
0
2
4
6
8
sqrt (time) (Months After Initial Production)^0.5
[email protected]
Impact of Terminal Decline Rate on EUR
Qelf  C p t elf  C p t Delf
D 
2 1
4 Tm
Q  C p
1
4
t Delf
Tm 
3
16
 2
c m  L
2
km
Cumulative Oil (bbl)
Q  Qelf

Tm
1
D
900000
800000
700000
600000
500000
400000
300000
200000
100000
0
0
2
4
6
8
sqrt (time) (Months After Initial Production)^0.5
[email protected]
Decline Rates: Summary
• Shale gas wells produce in the infinite acting regime for
many years.
For instantaneous drawdown, decline rate ~ 1/2t, independent of
the value of the production coefficient.
• Variable drawdown in the first few months of production
may reduce the early-time decline rate, and it is often
negative.
•Terminal decline rate is not arbitrary: it is related to the
time at the end of linear flow.
• Arps b-exponent varies in time in a rational manner.
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Some key results

Better characterized shale gas production through several
stages of production including early-time transient, linear flow
and boundary-dominated flow.

Methodology of presenting and analyzing production data using
an alternative metric for shale gas productivity—the Production
Coefficient.

Identified the major production drivers in terms of reservoir and
completion parameters.

Improved estimates of future production in comparison to
conventional techniques.

Demonstrated technique for estimating recovery from a limited
amount of production data.
[email protected]
Thank You!
Ian Walton, Ph.D.
Senior Research Scientist
[email protected]
Reserves Estimation Unconventionals
Houston August 2016
Project I 01024
[email protected]