defense presentation_ayush rastogi - 28 july 2014

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 – 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

• Developed a reliable method for type well construction to accuratelyforecast production for wells with less data

• Developed a method for removing the outliers and filtering the data fora better diagnostic analysis

• Created hybrid reservoir models to accurately forecast in unconventionalshale gas reservoirs taking into account different completion scenarios

Value

• Forecast accurately and obtain best estimates of ultimaterecovery

• More realistic reservoir models will help in achieving better resultsand hence aid in making better reservoir management decisions


Pseudo well – 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 ‘Sequence Bias’ addressed?

Is it possible to use a type well from one geographical area into another?


Type Well

Average Ra𝑡𝑒 = 𝑷𝒓𝒐𝒅 𝑹𝒂𝒕𝒆+ 𝑺𝑰 𝑹𝒂𝒕𝒆

# 𝑷𝒓𝒐𝒅+ # 𝑺𝑰

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 = Σ 𝐸𝑈𝑅

𝑁𝑜.𝑜𝑓 𝑤𝑒𝑙𝑙𝑠

Average Rate = 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 + Σ 𝑆ℎ𝑢𝑡−𝑖𝑛 𝑟𝑎𝑡𝑒

𝑁𝑜.𝑜𝑓 𝑝𝑟𝑜𝑑𝑢𝑐𝑖𝑛𝑔 𝑤𝑒𝑙𝑙𝑠+𝑁𝑜 𝑜𝑓 𝑆ℎ𝑢𝑡 𝑖𝑛 𝑤𝑒𝑙𝑙𝑠

Average Rate = 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒+ Σ 𝑆ℎ𝑢𝑡−𝑖𝑛 𝑟𝑎𝑡𝑒

𝑁𝑜.𝑜𝑓 𝑝𝑟𝑜𝑑𝑢𝑐𝑖𝑛𝑔 𝑤𝑒𝑙𝑙𝑠


Flaw in Averaging Method

Case 1 – Individual wells shut in at different times


Result – Johnson County (Barnett Shale)

• Known average EUR from 10 wells = 0.768 bcf

• EUR when shut-in wells included = 0.773 bcf

• 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

Shut in Period

Shut in Period

Case 2: Wells producing in different time periods


Result - Johnson County (Barnett Shale)

• Known Average EUR: = 0.793 bcf• Average EUR from common start time: = 0.798 bcf• 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

Starting Production in 2007

1

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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

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, msf

/m

Time, months


Monthly Production

Starting Production in 2004

Obtain production flow rate data

Neglect first few months of data

from the analysis – unstable

initial conditions

Determine average production

rate 𝑞𝑎𝑣𝑔 for first six months

Normalize individual well data by

using 𝑞/𝑞𝑎𝑣𝑔

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 – 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




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 – Normalization Method


Data – 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 – Normalization Method


Data – 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 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 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 – 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 ‘Sequence Bias’ 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


Diagnostic Plots

Uncertainty and non-uniqueness associated with the productiondata.

Outliers – 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:

• Synthetic data is created for which a thirty year forecast isalready known.

• Since it does not have any outliers, we try to plant outliers forfirst 2000 days, for which the data will be analyzed.

• A decline model: ‘YM-SEPD and Arps’ 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.

• The process is iterated until all outliers are removedsuccessfully.

• Three basic plots: q vs t, log q vs t, and log q vs MBT are usedto identify the flow regimes.

‘YM-SEPD + Arps’ Hybrid Model Equations

End of Linear Flow

𝑡𝑒𝑙𝑓 =𝐴ℎ 𝜙𝜇𝑐𝑡 𝑖𝑚 𝑝𝑝𝑖 − 𝑝𝑝𝑤𝑓

200.6 𝑇

2

SEPD Equation

𝑞 = 𝑞𝑜 × 𝑒𝑥𝑝[−𝑡

𝜏

𝑛

]

Yu Plot : ln(𝑞𝑜 /𝑞) 𝑣𝑠 𝑡𝑖𝑚𝑒

𝑦 = 𝑖𝑛𝑡 𝑥𝑛

𝜏 = exp[ − ln(𝑖𝑛𝑡))/𝑛

BDF:

𝑞 =𝑞𝑒𝑙𝑓

1 + 𝑏𝐷𝑒𝑙𝑓 𝑡 − 𝑡𝑒𝑙𝑓

1𝑏


Diagnostic Plots

0

500

1000

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

R² = 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


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

1000

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


Diagnostic Plots

0

1000

2000

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

10

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 – Bakken Shale Well

Consistency correlation - Rate and Pressure

Blasingame Type Curve NPI Type Curve

Diagnostic Plots Field Data – Bakken Shale Well

FMB Plot Fetkovich Type Curve


Hybrid Models

• Low to ultra-low permeability reservoirs produced from multi fractured horizontal reservoirs.

• Dominant Flow regimes: Linear FlowBoundary Dominated Flow

• For the case of hydraulically fractured wells, Arps exponent b, does not remain constant for transient, transitional and boundary dominated flow.

• Non unique forecasts obtained from various empirical models.

• ‘Hybrid’ 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.

• Hybrid model for two different types of completion are discussed in this work• Homogeneous Completion• Heterogeneous Completion


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.


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 – Homogeneous Completion

Variable Symbol Value

Initial Pressure, psi 𝑝𝑖 3,500

Reservoir Temperature, ℉ 𝑇𝑟 250

Net Pay, ft ℎ 300

Wellbore Radius, ft 𝑟𝑤 0.35

Porosity, % 𝜙 8

Initial Gas Saturation, % 𝑆𝑔𝑖 70

Initial Oil Saturation, % 𝑆𝑜𝑖 0

Initial Water Saturation, % 𝑆𝑤𝑖 30

Gas gravity 𝛾𝑔 0.65

Initial total compressibility,

1/psi

𝑐𝑡𝑖 2.02E-04

Initial Pressure, psi 𝑝𝑖 3,500

Fracture Half Length, ft 𝑥𝑓 𝑦200

Effective Horizontal Well Length, ft 𝐿𝑒 2,000

Fracture Conductivity 𝐹𝑐𝑑 350

Number of Fractures 𝑛𝑓 15

Inner Zone Permeability, md 𝐾1 4.00E-05

Net Pay, ft ℎ 300

Porosity, % 𝜙 8

Reservoir Length, ft 𝑋𝑒 2,000

Reservoir Width, ft 𝑌𝑒 400

Area of SRV, acres 𝐴𝑠𝑟𝑣 18

Original Gas in Place 𝑂𝐺𝐼𝑃 2,475


Initial Sandface Pressure, psi 𝑝𝑤𝑓 500


Hybrid Model Validation – Homogeneous Completion

‘Linear + Arps’

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 ‘Linear + Arps’ Hybrid Model

Production Data Synthetic Forecast 'Linear + Arps' Forecast Linear Forecast

Parameters:

𝑚 = 8 × 10−5

𝑏′ = 0.0001478



‘Duong + Arps’

Parameters:

𝑎 = 1.060𝑚 = −1.11

𝑞1 = 23,210 𝑚𝑠𝑐𝑓/𝑚𝑞∞ = 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

500

600

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



‘YM-SEPD + Arps’

Parameters:

𝑛 = 0.5736𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 = 0.0138𝜏 = 1749.39

y = 0.0138x0.5736

R² = 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



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




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 – Heterogeneous Completion

Variable Symbol Value

Initial Pressure, psi 𝑝𝑖 3500

Reservoir Temperature,

℉𝑇𝑟 250

Net Pay, ft ℎ 300


Porosity, % 𝜙 10

Initial Gas Saturation,

%𝑆𝑔𝑖 80

Initial Oil Saturation, % 𝑆𝑜𝑖 0

Initial Water Saturation,

%𝑆𝑤𝑖 20

Gas gravity 𝛾𝑔 0.65

Initial total

compressibility, 1/psi𝑐𝑡𝑖 2.02E-04

Fracture Conductivity 𝐹𝑐𝑑 350

Number of Fractures 𝑛𝑓 5

Permeability in x direction, md 𝐾𝑥 1.00E-04

Permeability in y direction, md 𝐾𝑦 1.00E-04

delta xf (xf)y Fcd

-800 300 350

-400 100 350

0 200 350

400 200 350

800 300 350


Hybrid Model Validation – Heterogeneous Completion

‘Linear + Arps’

𝑞 = 𝑞𝑗 = 𝛾𝑗 (1 − 𝑈 𝑡 − 𝑡𝑒𝑙𝑓

𝑚 𝑡+

𝑈 𝑡 − 𝑡𝑒𝑙𝑓

𝑚 𝑡𝑒𝑙𝑓 1 + 𝑏𝐷𝑒𝑙𝑓 𝑡 − 𝑡𝑒𝑙𝑓

1𝑏

]

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



‘Duong + Arps’

𝑞 =

𝑗=1

𝑟

𝑞𝑗 =

𝑗=1

𝑟

𝛼𝑗𝛽𝑗𝑞𝐷𝑢𝑜𝑛𝑔 1 − 𝑈 𝑡 − 𝑡𝑒𝑙𝑓𝑗 +𝑞𝑒𝑙𝑓𝑗

1 + 𝑏𝐷𝑒𝑙𝑓𝑗 𝑡 − 𝑡𝑒𝑙𝑓𝑗

1𝑏

𝑈 𝑡 − 𝑡𝑒𝑙𝑓𝑗

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



‘YM-SEPD + Arps’

𝑞 =

𝑗=1

𝑟

𝑞𝑗 =

𝑗=1

𝑟

𝛼𝑗𝛽𝑗𝑞𝑌𝑀𝑆𝐸𝑃𝐷 1 − 𝑈 𝑡 − 𝑡𝑒𝑙𝑓𝑗 +𝑞𝑒𝑙𝑓𝑗

1 + 𝑏𝐷𝑒𝑙𝑓𝑗 𝑡 − 𝑡𝑒𝑙𝑓𝑗

1𝑏

𝑈 𝑡 − 𝑡𝑒𝑙𝑓𝑗

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




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




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

defense presentation_ayush rastogi - 28 july 2014

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