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Production Forecasting using Type Wells, Diagnostic Plots and
Hybrid Models
Ayush RastogiUniversity of Houston
Thesis Defense, Masters
July 28, 2014
Problem
Unconventional Shale Gas Reservoirs β low to ultra low permeability
longer transient periods
Conventional decline curve methods are not applicable
Incorrect EUR values from unconventional shale reservoirs
Estimating reserves for fields with less data
Incorrect diagnostic methods may lead to misinterpretation
Need for hybrid reservoir models to forecast wells with differentcompletion scenarios
9/10/2014 2University of Houston
Solution and Value
Solution
β’ Developed a reliable method for type well construction to accuratelyforecast production for wells with less data
β’ Developed a method for removing the outliers and filtering the data fora better diagnostic analysis
β’ Created hybrid reservoir models to accurately forecast in unconventionalshale gas reservoirs taking into account different completion scenarios
Value
β’ Forecast accurately and obtain best estimates of ultimaterecovery
β’ More realistic reservoir models will help in achieving better resultsand hence aid in making better reservoir management decisions
9/10/2014 3University of Houston
Pseudo well β Created by averaging production rate of many wells and used to determine the production rate from new well(s) based on performance of several analogous wells
Issues
Production of how many wells is required to construct a type well?
How is the issue of βSequence Biasβ addressed?
Is it possible to use a type well from one geographical area into another?
9/10/2014 4University of Houston
Type Well
Average Raπ‘π = π·πππ πΉπππ+ πΊπ° πΉπππ
# π·πππ + # πΊπ°
Correct method: Production of all wells considered, irrespective of being shut in
Incorrect method: Ignoring count of shut in wells for calculating average rate of type wells
Average Rate = Ξ£ πΈππ
ππ.ππ π€ππππ
Average Rate = πππππ’ππ‘πππ π ππ‘π + Ξ£ πβπ’π‘βππ πππ‘π
ππ.ππ πππππ’ππππ π€ππππ +ππ ππ πβπ’π‘ ππ π€ππππ
Average Rate = πππππ’ππ‘πππ π ππ‘π+ Ξ£ πβπ’π‘βππ πππ‘π
ππ.ππ πππππ’ππππ π€ππππ
9/10/2014 5University of Houston
Flaw in Averaging Method
Case 1 β Individual wells shut in at different times
9/10/2014 6University of Houston
Result β Johnson County (Barnett Shale)
β’ Known average EUR from 10 wells = 0.768 bcf
β’ EUR when shut-in wells included = 0.773 bcf
β’ EUR when shut-in wells excluded = 1.639 bcf
1
10
100
1000
10000
100000
May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13 Dec-14
Mo
nth
ly P
rod
uct
ion
, msf
/m
Time, months
Monthly Production - Johnson County, Barnett Shale
Monthly Production
1
10
100
1000
10000
100000
May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13 Dec-14
Mo
nth
ly P
rod
uct
ion
, msf
/m
Time, months
Monthly Production - Johnson County, Barnett Shale
Monthly Production
Shut in Period
Shut in Period
Case 2: Wells producing in different time periods
9/10/2014 7University of Houston
Result - Johnson County (Barnett Shale)
β’ Known Average EUR: = 0.793 bcfβ’ Average EUR from common start time: = 0.798 bcfβ’ Average EUR from different start time = 1.169 bcf
1
10
100
1000
10000
100000
1000000
Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13 Dec-14
Mo
nth
ly P
rod
uct
ion
, msf
/m
Time, months
Monthly Production - Johnson County, Barnett Shale
Monthly Production
Starting Production in 2007
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1000
10000
100000
Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13
Mo
nth
ly P
rod
uct
ion
, msf
/m
Time, months
Monthly Production - Johnson County, Barnett Shale
Monthly Production
Starting Production in 2004
Obtain production flow rate data
Neglect first few months of data
from the analysis β unstable
initial conditions
Determine average production
rate πππ£π for first six months
Normalize individual well data by
using π/πππ£π
By averaging individual well
normalized rates determine
normalized type well rates
Using normalized type well rates
and qavg for individual well,
forecast individual well rates to
the end of type well period
8
Individual wells differ significantly from correct average well
Solution β Normalization
1
10
100
1000
10000
100000
0 500 1000 1500 2000
q m
scf/
d
Time (days)
Data_Denton
Well 1 Well 2 Well 3 Well 4
Well 5 Well 6 Well 7 Well 8
Well 9 Well 10 Well 11 Well 12
Well 13 Well 14 Well 15 Well 16
0.01
0.1
1
10
0 500 1000 1500 2000 2500 3000 3500 4000
q/q
avg
time, days
Normalized Rate Comparison - Log q/qavg vs time
Well 1 Well 2 Well 3 Well 4Well 5 Well 6 Well 7 Well 8Well 9 Well 10 Type Well
Validation β Normalization Method
9/10/2014 9University of Houston
Data β Tarrant County, Barnett Shale
0
5000
10000
15000
20000
25000
30000
0 500 1000 1500 2000 2500 3000 3500 4000
q, m
scfd
time, days
Rate Comparison - q vs t
Rate Forecast from Type Well Production Data
10000
100000
1000000
10000000
0 500 1000 1500 2000 2500 3000 3500 4000
Q, m
scf
time, days
Cumulative Production Comparison - log Q vs t
Cumulative Production Cumulative Production from Type Well
time, days 500 1,000 1,500 2,000 2,500 3,000 3,500 3,865
Actual
Cumulative (mscf)202,678 419,883 595,689 741,068 884,469 988,847 1,086,775 1,162,632
Estimated Cumulative
from Type Well (mscf)
207,136 416,578 587,947 722,465 848,618 944,008 1,027,464 1,081,281
Difference (%)2.1 0.8 1.3 2.5 4.0 4.5 5.4 6.9
RESULTS
Validation β Normalization Method
9/10/2014 10University of Houston
Data β Denton County, Barnett Shale
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
0 500 1000 1500 2000 2500 3000 3500 4000
q, m
scfd
time, days
Rate Comparison - q vs t
Rate Forecast from Type Well Rate - Individual Data
10000
100000
1000000
0 500 1000 1500 2000 2500 3000 3500 4000
Q, m
scf
time, days
Cumulative Production Comparison - log Q vs t
Cumulative Production Cumulative Production from Type Well
time, days 500 1,000 1,500 2,000 2,500 3,000 3,500 3,865
Actual Cumulative
(mscf) 138,459 299,386 400,640 461,252 514,688 554,992 590,201 602,072
Estimated Cumulative
from Type Well (mscf) 134,708 276,210 386,390 464,160 531,830 584,208 630,180 646,000
Difference, %2.7 7.7 3.5 0.6 3.3 5.2 6.7 7.2
RESULTS
9/10/2014 11
Normalized Type Well Similar for Different Counties in Barnett Shale
University of Houston
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 500 1000 1500 2000 2500 3000 3500 4000
q, m
scfd
time, days
Type Well Comparison between different counties - Tarrant and Denton
Denton Type Well Tarrant Type Well
0
5000
10000
15000
20000
25000
30000
35000
0 500 1000 1500 2000 2500 3000 3500 4000
q, m
scfd
time, days
Rate Comparison - q vs t
Rate Forecast - Denton Type Well Production data Rate Forecast - Tarrant Type Well
time, days
500 1,000 1,500 2,000 2,500 3,000 3,500 3,865
Difference, %
Actual
Cumulative,
mscf
192,909 359,441 498,400 638,825 794,384 906,799 1,008,227 1,023,737
Estimated
Cumulative
from Type
Well - Denton
211,733 434,144 607,325 729,562 835,925 918,253 990,512 1,015,3780.8
Estimated
Cumulative
from Type
Well - Tarrant
210,334 423,011 597,026 733,622 861,723 958,585 1,043,330 1,071,3784.6
RESULTS
Data β Tarrant and Denton County, Barnett Shale
Issues Revisited
Production of how many wells is required to construct a type well?
The larger the number of wells, the better would the type well be. At least ten wells should be used for analysis.
How is the issue of βSequence Biasβ addressed?
Truncate the wells which have large data gap and consider those wells which produce for same time periods.
Is it possible to use a type well from one geographical area into another?
Type wells from one area can be used to estimate rates for wells in another area with same geological conditions.
9/10/2014 University of Houston 12
9/10/2014 University of Houston 13
Diagnostic Plots
Uncertainty and non-uniqueness associated with the productiondata.
Outliers β may produce a false unit slope which might bemisinterpreted as reservoir depletion. Should not be used as anindication of boundary dominated flow.
Procedure applied to remove outliers:
β’ Synthetic data is created for which a thirty year forecast isalready known.
β’ Since it does not have any outliers, we try to plant outliers forfirst 2000 days, for which the data will be analyzed.
β’ A decline model: βYM-SEPD and Arpsβ hybrid is used to obtain afit through the data. Assuming the data points are normallydistributed, we try to remove the points lying one standarddeviation from the fit. This is done to make sure that outliersare identified even at low rates.
β’ The process is iterated until all outliers are removedsuccessfully.
β’ Three basic plots: q vs t, log q vs t, and log q vs MBT are usedto identify the flow regimes.
βYM-SEPD + Arpsβ Hybrid Model Equations
End of Linear Flow
π‘πππ =π΄β ππππ‘ ππ πππ β πππ€π
200.6 π
2
SEPD Equation
π = ππ Γ ππ₯π[βπ‘
π
π
]
Yu Plot : ln(ππ /π) π£π π‘πππ
π¦ = πππ‘ π₯π
π = exp[ β ln(πππ‘))/π
BDF:
π =ππππ
1 + ππ·πππ π‘ β π‘πππ
1π
9/10/2014 University of Houston 14
Diagnostic Plots
0
500
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1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500
q, m
scfd
time, days
Comparison of synthetic and modified dataset
Synthetic Data
0
500
1000
1500
2000
2500
3000
3500
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
YM SEPD and Arps Hybrid Forecast
Production Data YM-SEPD + Arps Synthetic Forecast
y = 0.0115x0.5997
RΒ² = 0.99930.01
0.1
1
10
100 1000 10000
ln (
qo
/q)
time, days
Yu Plot
Data for Yu Plot Synthetic Data Power (Data for Yu Plot)
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500
q, m
scfd
time, days
Comparison of synthetic data, modified data and filtered dataset
Synthetic Data Modified Data with outliers Filtered Data
9/10/2014 University of Houston 15
0
500
1000
1500
2000
2500
3000
3500
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
YM SEPD and Arps Hybrid Forecast
Production Data YM-SEPD + Arps Synthetic Forecast
Diagnostic Plots
Synthetic
Dataset
Modified
Dataset
Filtered
Dataset
30 Year
Cumulative
Volumes, mscf199,081 173,392 198,232
Difference, %12.90
Flow Regimes
100
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10000
100 1000 10000 100000
q, m
scfd
time, days
log q vs log t Rate
Slope -1/2: Linear
Slope -1: BDF
100
1000
10000
100 1000 10000 100000
q, m
scfd
MBT, days
log q vs MBTSynthetic Production Data
Slope -1: BDF
Slope -1/2: Linear
9/10/2014 University of Houston 16
Diagnostic Plots
0
1000
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3000
4000
5000
0 100 200 300 400 500 600 700 800
q, m
scfd
time,days
Original and Filtered Data Comparison
Production Data Filtered Data
Noise
0
1000
2000
3000
4000
5000
100 200 300 400 500 600 700 800
q, m
scfd
time, days
q vs t
Production Data
100
1000
10000
100 1000
q, m
scfd
time, days
log q vs log t
Production Data
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100
1000
10000
100 1000 10000
q, m
scfd
MBT, days
q vs MBT
q vs MBT
Slope -1: BDF
Slope -1/2: Linear
Field Data β Bakken Shale Well
Consistency correlation - Rate and Pressure
Blasingame Type Curve NPI Type Curve
Diagnostic Plots Field Data β Bakken Shale Well
FMB Plot Fetkovich Type Curve
9/10/2014 University of Houston 18
Hybrid Models
β’ Low to ultra-low permeability reservoirs produced from multi fractured horizontal reservoirs.
β’ Dominant Flow regimes: Linear FlowBoundary Dominated Flow
β’ For the case of hydraulically fractured wells, Arps exponent b, does not remain constant for transient, transitional and boundary dominated flow.
β’ Non unique forecasts obtained from various empirical models.
β’ βHybridβ model forecasting techniques for multi fractured horizontal wells combine analytical methods for forecasting transient and transitional flow along with empirical methods for forecasting boundary dominated flow.
β’ Hybrid model for two different types of completion are discussed in this workβ’ Homogeneous Completionβ’ Heterogeneous Completion
9/10/2014 University of Houston 19
Homogeneous Completion
Every hydraulic fracture connected to a perforated cluster is equal in length and height.
Fractures spaced in an orderly fashion, with equal distances between them
Issue:
Equal length fractures are rarely created. The cause might be multiple clusters in ahydraulic fracture stage, localized stress heterogeneities within a reservoir,differences in perforation effectiveness and differences in localized leak-off.
9/10/2014 University of Houston 20
Heterogeneous Completion
Fractures are of unequal length, spaced in an orderly fashion with equal distances between them.
In this case, since the fracture lengths aredifferent, the end of linear flow occurs atdifferent times. Some regions will enterthe boundary dominated flow regime,whereas the others might still be in thelinear flow regime.
This model is a more realisticrepresentation of actual completion withina MFHW.
Synthetic Data β Homogeneous Completion
Variable Symbol Value
Initial Pressure, psi ππ 3,500
Reservoir Temperature, β ππ 250
Net Pay, ft β 300
Wellbore Radius, ft ππ€ 0.35
Porosity, % π 8
Initial Gas Saturation, % πππ 70
Initial Oil Saturation, % πππ 0
Initial Water Saturation, % ππ€π 30
Gas gravity πΎπ 0.65
Initial total compressibility,
1/psi
ππ‘π 2.02E-04
Initial Pressure, psi ππ 3,500
Fracture Half Length, ft π₯π π¦200
Effective Horizontal Well Length, ft πΏπ 2,000
Fracture Conductivity πΉππ 350
Number of Fractures ππ 15
Inner Zone Permeability, md πΎ1 4.00E-05
Net Pay, ft β 300
Porosity, % π 8
Reservoir Length, ft ππ 2,000
Reservoir Width, ft ππ 400
Area of SRV, acres π΄π ππ£ 18
Original Gas in Place ππΊπΌπ 2,475
Wellbore Radius, ft ππ€ 0.35
Initial Sandface Pressure, psi ππ€π 500
9/10/2014 University of Houston 22
Hybrid Model Validation β Homogeneous Completion
βLinear + Arpsβ
0
0.0005
0.001
0.0015
0.002
0.0025
0 10 20 30
1/q
, 1/m
scfd
t^0.5, days
(1/q vs t^0.5, for first year of data)
10
110
210
310
410
510
610
710
810
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000
q, m
scfd
Time (days)
Forecast for βLinear + Arpsβ Hybrid Model
Production Data Synthetic Forecast 'Linear + Arps' Forecast Linear Forecast
Parameters:
π = 8 Γ 10β5
πβ² = 0.0001478
9/10/2014 University of Houston 23
Hybrid Model Validation β Homogeneous Completion
βDuong + Arpsβ
Parameters:
π = 1.060π = β1.11
π1 = 23,210 ππ ππ/ππβ = 0
0.001
0.01
0.1
1
1 10 100
q/G
p, 1
/mo
nth
Time, Monthsq/Gp Duong
0
100
200
300
400
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700
800
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
Forecast for 'Duong + Arps' Hybrid Model
Production Data Synthetic Forecast Duong Forecast Duong + Arps Forecast
9/10/2014 University of Houston 24
Hybrid Model Validation β Homogeneous Completion
βYM-SEPD + Arpsβ
Parameters:
π = 0.5736πππ‘ππππππ‘ = 0.0138π = 1749.39
y = 0.0138x0.5736
RΒ² = 0.9998
0.1
1
10
1000 10000 100000
ln (
qo
/q)
time, days
Yu Plot
Data for Yu Plot Synthetic Data Power (Data for Yu Plot)
0
100
200
300
400
500
600
700
800
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
Forecast for 'YM-SEPD + Arps' Hybrid Model
YM SEPD Synthetic Forecast YM-SEPD + Arps Production Data
9/10/2014 University of Houston 25
Hybrid Model Validation β Homogeneous Completion
Comparison between different hybrid models with synthetic data
10
110
210
310
410
510
610
710
810
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000
q, m
scfd
Time (days)
Rate Comparison - Simulated Data vs Hybrid Model Forecast
Production Data Synthetic Forecast Linear + Arps Duong+Arps YM-SEPD + Arps
9/10/2014 University of Houston 26
Hybrid Model Validation β Homogeneous Completion
Comparison between different hybrid models with synthetic data
100
1000
10000
100000
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000
Q, m
scf
Time (days)
Cumulative Rate Comparison - Simulated Data vs Hybrid Model Forecast
Production Data Synthetic Data Linear + Arps Forecast Duong + Arps Forecast SEPD+Arps Forecast
Cumulative Production mscf Difference %
Cumulative (Synthetic Data - After first 6 months) 55,990
Cumulative (Linear + Arps) 70,058 25.1
Cumulative (Duong + Arps) 66,789 19.2
Cumulative (YM-SEPD + Arps) 51,993 7.1
Synthetic Data β Heterogeneous Completion
Variable Symbol Value
Initial Pressure, psi ππ 3500
Reservoir Temperature,
βππ 250
Net Pay, ft β 300
Wellbore Radius, ft ππ€ 0.35
Porosity, % π 10
Initial Gas Saturation,
%πππ 80
Initial Oil Saturation, % πππ 0
Initial Water Saturation,
%ππ€π 20
Gas gravity πΎπ 0.65
Initial total
compressibility, 1/psiππ‘π 2.02E-04
Fracture Conductivity πΉππ 350
Number of Fractures ππ 5
Permeability in x direction, md πΎπ₯ 1.00E-04
Permeability in y direction, md πΎπ¦ 1.00E-04
delta xf (xf)y Fcd
-800 300 350
-400 100 350
0 200 350
400 200 350
800 300 350
9/10/2014 University of Houston 28
Hybrid Model Validation β Heterogeneous Completion
βLinear + Arpsβ
π = ππ = πΎπ (1 β π π‘ β π‘πππ
π π‘+
π π‘ β π‘πππ
π π‘πππ 1 + ππ·πππ π‘ β π‘πππ
1π
]
0
500
1000
1500
2000
2500
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
'Linear + Arps' - Heterogeneous
Synthetic Forecast Linear + Arps Linear Forecast Production Data
9/10/2014 University of Houston 29
Hybrid Model Validation β Heterogeneous Completion
βDuong + Arpsβ
π =
π=1
π
ππ =
π=1
π
πΌππ½πππ·π’πππ 1 β π π‘ β π‘ππππ +πππππ
1 + ππ·ππππ π‘ β π‘ππππ
1π
π π‘ β π‘ππππ
0
500
1000
1500
2000
2500
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
'Duong + Arps'- Heterogeneous
Synthetic Forecast Duong Production Data Duong+Arps
9/10/2014 University of Houston 30
Hybrid Model Validation β Heterogeneous Completion
βYM-SEPD + Arpsβ
π =
π=1
π
ππ =
π=1
π
πΌππ½ππππππΈππ· 1 β π π‘ β π‘ππππ +πππππ
1 + ππ·ππππ π‘ β π‘ππππ
1π
π π‘ β π‘ππππ
0
500
1000
1500
2000
2500
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
'YM-SEPD + Arps'
YM-SEPD Synthetic Forecast Production YMSEPD+Arps
9/10/2014 University of Houston 31
Hybrid Model Validation β Heterogeneous Completion
Comparison between different hybrid models with synthetic data
0
500
1000
1500
2000
2500
0 2000 4000 6000 8000 10000 12000
q, m
scfd
time, days
Rate Comparison - Synthetic Forecast and Hybrid model - Heterogeneous Completion
Linear + Arps Synthetic Duong+Arps YMSEPD+ Arps Production
9/10/2014 University of Houston 32
Hybrid Model Validation β Homogeneous Completion
Comparison between different hybrid models with synthetic data
0
20000
40000
60000
80000
100000
120000
140000
160000
0 2000 4000 6000 8000 10000 12000
Q, m
scf
time, days
Cumulative Comparison - Synthetic Forecast and Hybrid model - Heterogeneous Completion
Linear + Arps Synthetic Duong+Arps YM SEPD + Arps Production
Cumulative Production mscf Difference %
Cumulative (Synthetic Data) 128,596
Cumulative (Linear + Arps) 137,101 6.6
Cumulative (Duong + Arps) 133392 3.7
Cumulative (YM-SEPD + Arps) 130245 1.2
Current averaging method used in industry by some professionals to construct Type Wells is flawed
Shut-in wells should continued to be counted
Wells should be normalized to same shut-in time
Type wells can be a useful tool to forecast rates till the end of type well history. This case is especially useful for the wells which do not have enough production history.
Normalization method to construct Type Wells yields best results
Type wells constructed from one geographic area can be used to obtain rates from another area.
Issues with inconsistent data should be sorted before proceeding to analysis stage. The suggested method of outlier analysis can be used to filter unwanted data successfully.
Use of single decline model to forecast unconventional reservoirs leads to incorrect estimates. A hybrid model of YM-SEPD transient model and Arps hyperbolic model with a b = 0.5 works best for homogeneous and heterogeneous completions.
Conclusions
Production Forecasting using Type Wells, Diagnostic Plots and
Hybrid Models
Ayush Rastogi
University of Houston
Thesis Defense, Masters
July 28, 2014