based on actual production data (gas water ratio plotting) · 1. synthetic data of cbm reservoir...

Abstract - Energy demand in the world in general and in

Indonesia in particular is growing rapidly. Coal Bed Methane

(CBM-Coalbed Methane) can be used as an alternative energy

accompanying fossil energy (oil and gas). Indonesian CBM

resources could potentially have a very large (estimated at 450

TSCF) are spread in various islands in Indonesia. CBM

exploitation and development methods (unconventional gas) is

different from the usual in gas reservoirs (conventional gas). So

many methods or technical issues that still need to be

investigated. Forecasting the behavior (performance)

production of both gas and water in the reservoir

unconventional Coalbed Methane (CBM) reservoir found at the

beginning or during production is always necessary to be able to

better manage the future reservoir. The flow rate is a function

of reservoir characteristics of coal (both static and dynamic

characteristics). The purpose of this research is to develop

methods and procedures or guidelines (guidelines) in predicting

the flow rate of gas and water, based on a plotting function for

example: Cumulative Water Gas Ratio, Gp (Cumulative Gas

Production), and time, for a production that has been advanced

(mature),

Keywords : Coalbed Methane, Gas Water Ratio Plotting,

Production Performance Prediction,

I.INTRODUCTION

After various experiments conducted on the development of

the plots of this method GWR vs. Time, it produces a straight

line on a linear scale while the QW vs. Time, produces a

straight line on a logarithmic scale, so that the two are plotting

the results, it can be used as a method of production flow rate

prediction in the future integrated (gas and water) at the time

of the gas

and the water has undergone initial production decline.

Difference of this method compared with the development of

Decline Curve method that has been widely used for this is

the gas flow rate and water in an integrated predictable. Water

flow rate is also predicted also can be used to anticipate the

amount of water produced, so that the corresponding surface

facility (water treatment, etc.) can be prepared in advance.

Figure 1 shows a flow diagram of the development methods

in this study.

The data used for the calculation of the study are:

Plotting of Production Ratio (Gas - Water Ratio Plotting).

1. Single wellbore Simulation Model for Plotting GWR.

2. Linearity of CBM Pilot Performance for GWR Plot -

Indian Field Data (Pilot Project).

3. Linearity of CBM-Field Data for GWR Plot - Fekette's

Field Data.

4. Linearity of CBM-Field Data for GWR Plot – the “Z”

Basin, Colorado Field’s Data (5 wells).

Method of Testing Results

The results of the development of this method using data

CBM field in America and India. Necessary to explain

that the data from the field of CBM in Indonesia has not

been available until now, because there is no field of CBM

production.

Figure 1 Procedure of The Prediction of CBM production profiles.

Plotting Production Ratio Method

The purpose of this method is to find a relationship or

function, in order to obtain gas production forecasting and

integrated water. This method is different from the "Decline

Curve", which on the prediction method to predict gas and

water separately.

Production Performance Prediction in

Coalbed Methane Reservoir Based on Actual Production Data (Gas Water Ratio Plotting)

*)Ratnayu Sitaresmi

*)Department of Petroleum Engineering,

Faculty of Earth Technology and Energy,

Trisakti University

Jakarta, Indonesia

**) Doddy Abdassah and Dedy Irawan**)

Department of Petroleum Engineering,

Faculty of Mining and Petroleum Engineering,

Bandung Institute of Technology

Bandung, Indonesia

International Journal of Engineering Research & Technology (IJERT)

ISSN: 2278-0181

www.ijert.orgIJERTV4IS030031

(This work is licensed under a Creative Commons Attribution 4.0 International License.)

Vol. 4 Issue 03, March-2015

1153

1. Synthetic Data of CBM Reservoir Simulation in the Field

'X' Kaltim.

This method is used to test the reservoir simulator with

the input data used for the East Kalimantan region (sub-

bituminous).

In Figure 2 is a simulation model of the field 'X' in East

Kalimantan, which will predict the water flow rate and

gas phase when using GWR Plotting decline curve.

Figure 2 CBM reservoir data from the field 'X' East Kalimantan.

In Figure 3 is a CBM production profiles with the

predictions of its gas and water rate by using a reservoir

simulator.

Seen in Figure 4 plots the trendline from the Water Gas

Rate with Time is a straight line on a linear scale. The

results of the line equation: Y = X-9.97609E +00 8.1875E

+02.

Seen in Figure 5 plots the trendline from the Water Rate

with Time is a straight line on a logarithmic scale. The

results of the line equation: Y = X ^ 1.26209E +09

2.30793E +00

In Figure 6 is a comparison between the proposed method

(GWR - Water Rate Plotting) Prediction with simulation

results. Results of the two curves are very comparable.

This indicates that GWR - Water

Figure 3. CBM production profile simulation results in the field 'X'

East Kalimantan

Figure 4 Trendline from water gas ratio vs. time

Rate decent Plotting used to predict the rate of gas-water

wells have been in production conditions to reach peak or

maximum production and production is in decline

(decline curve).

Figure 5 Trendline of water rate vs. time

0

2

4

6

8

10

12

14

16

18

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10/10/2006 22/09/2017 04/09/2028 18/08/2039 31/07/2050 13/07/2061

WA

TE

R R

AT

E,

BB

L/D

GA

S R

AT

E, S

CF

/D

DATE

CBM PRODUCTION PROFIL

Gas Rate SC

Water Rate SC


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1154

Figure 6 Predicted behavior of CBM produc-tion in the field 'X' East

Kalimantan.

2. Linearity of CBM Pilot Performance for GWR Plot-

Indian Field Data In Figure 7 is the actual production

profiles of wells from CBM pilot project in India. In the

pilot project, the gas production peaked after

approximately 5 and after that began to decline in

production. Water production began to fall in the

months to.

3. In Figure 8a is a plot between GWR vs. Time, while

Figure 8b is from GWR Plotting trendline indicating a

linear line.

Figure 7 Profile of CBM production in the field 'Y' India.

Figure 8 Trendline from water gas ratio vs. time.

In Figure 9a is a plot between the Water Rate vs. Time,

while Figure 9b is the trendline from the Water Rate

Plotting showing linear line on a log scale.

Figure 9 Trendline of water rate vs. time.

In Figure 10 is a production history followed by Gas-

Water Rate Prediction results of the proposed methods

wells 'Y' of CBM in India namely GWR - Water Rate

Plotting.

Figure 10 Predicted behavior of CBM production in the field 'Y' India.

4. Linearity of CBM-Field Data for GWR Plot-Colorado's

Field Data.

-

100

200

300

400

500

600

0

50,000

100,000

150,000

200,000

250,000

300,000

10/10/2006 12/27/2014 3/15/2023 6/1/2031 8/18/2039 11/4/2047 1/21/2056

WA

TE

R R

AT

E, B

BL

/D

GA

S R

AT

E, S

CF

/D

DATE

CBM PRODUCTION PROFIL

GAS RATE, SCF/D

GAS RATE PREDICTION

WATER RATE, BBL/D

IGIP = 2.36 BSCFD

GP = 1.75 BSCFD

RF = 74.2 %

Assumed Abandonment Rate

Qgas = 27.4 MSCF/D

PREDICTION

PRODUCTION

HISTORY

CBM Well Production

-

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

23-06-2000 01-10-2000 09-01-2001 19-04-2001 28-07-2001 05-11-2001 13-02-2002

Gas R

ate

, scfd

-

10

20

30

40

50

60

70

80

90

Wate

r ra

te, b

wp

d

Gas Rate, scfd

Water Rate, bw pd


ISSN: 2278-0181




1155

There are 4 pieces CBM wells into data from Water Gas

Ratio Method in the field plotting “Z”, Colorado.

a. Plotting Water Gas Ratio Method for Colorado's

well CBM Field Data

In Figure 11 is the actual production profile of the

well No. 7 from the field of CBM in the “Z” Basin,

Colorado Field, where his gas-water rate will be

predictable.

Figure 11 Profile of CBM production wells 7 Colorado Field.

In Figure 12 is a plot between GWR vs. Time, well

no 7 from the field in the “Z” Basin, CBM Field,

Colorado Field which produces a linear line.

In Figure 13 is a plot between the Water Rate vs.

Time, well no 7 from the field in the “Z” Basin CBM,

Colorado Field which produces a linear line on a

loga-rithmic scale.

Figure 12 Trendline from water gas ratio vs. time well 7

Colorado.

Figure 13 Trendline from water gas ratio vs. time well 7

Colorado.

In Figure 14, a production history followed by Gas-

Water Rate Prediction results of the proposed

methods wells No. 7 from the field in the “Z” Basin

CBM well, Colorado Field the GWR - Water Rate

Plotting.

Figure 14 Predicted behavior of CBM production wells 7

Colorado.

b. Plotting Water Gas Ratio Method at 19 CBM wells

Colorado's Field

In Figure 15 is the actual production profile of the well

no 19 from the field in “Z” Basin, CBM Field,

Colorado Field, where his gas-water rate will be

predictable.

c. In Figure 16 is a plot between GWR vs. Time, wells no

19 from the field in the “Z” Basin, CBM well, Colorado

Field which produces a linear line.

d. In Figure 17 is a plot between the Water Rate vs. Time,

wells no 19 from the field in the “Z” Basin, CBM Well,

Colorado Field which produces a linear line on a loga-

rithmic scale.

0

500

1,000

1,500

2,000

2,500

3,000

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

8/11/87 1/31/93 7/24/98 1/14/04 7/6/09 12/27/14

WA

TE

R R

AT

E, B

BL/D

GA

S R

AT

E, S

CF

/D

DATE

CBM PRODUCTION PROFIL - Well 7

Gas Rate, SCF/D

Water Rate, BBL/D

y = -90.9ln(x) + 928.3R² = 0.012

0

50

100

150

200

250

300

4,000 5,000 6,000 7,000 8,000 9,000 10,000

WA

TE

R R

AT

E, B

BL/D

TIME, DAYS

TIME vs WATER RATE

Water Rate, BBL/D

Logarithmic Regression


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Figure 15 Profiles 19 CBM production wells Colorado.

In Figure 18, a production history followed by Gas-

Water Rate Prediction results of the proposed methods

wells no 19 from

the

Figure 16 Trendline from water gas ratio vs. time 19 wells Colorado.

field in the “Z” Basin, CBM Well, Colorado Field the

GWR - Water Rate Plotting.

e. Plotting Water Gas Ratio Method 11 CBM wells

Colorado's Field In Figure 19 is the actual production

profile of the well no 11

Figure 17 Water rate vs. time 19 wells Colorado.

from the field in the “Z” Basin, CBM Field, Colorado

Field, where his gas-water rate will be predictable.

Fi

gure 18 Predicted behavior of CBM production wells 19 Colorado.

In the figure 20 is a plot between GWR vs. Time, wells

no 11 from the field in the “Z” Basin, CBM Field,

Colorado Field produces a linear line.

Figure 19 Profiles 11 CBM production wells Colorado.

In Figure 21 is a plot between the Water Rate vs. Time, wells

no 11 from the field in the “Z” Basin, CBM Field, Colorado

Field which produces a linear line on a logarithmic scale.

In Figure 22, a production history followed by Gas-Water

Rate Prediction results of the proposed methods wells no 11

from the field in The “Z” Basin, CBM Field, Colorado Field

the GWR - Water Rate Plotting.

0

20

40

60

80

100

120

140

160

180

200

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

12/6/99 4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09W

AT

ER

RA

TE

, B

BL/D

GA

S R

AT

E

SC

F/D

DATE

CBM PRODUCTION FORCAST - Well 19

Gas Rate, SCF/D

Water Rate, BBL/D

y = -4.508x + 71903R² = 0.014

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

0 500 1000 1500 2000 2500 3000 3500

GA

S W

AT

ER

R

AT

IO

TIME, DAYS

TIME vs GAS WATER RATIO - Well 19

Gas-Water Ratio

Linear Regression

y = -4.45ln(x) + 43.59R² = 0.146

0

10

20

30

40

50

60

0 500 1000 1500 2000 2500 3000 3500

WA

TE

R R

AT

E, B

BL/D

TIME, DAYS

TIME vs WATER RATE - Well 19

Water Rate, BBL/D

0

20

40

60

80

100

120

140

160

180

200

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09

WA

TE

R R

AT

E, B

BL/D

GA

S R

AT

E, S

CF

/D

DATE


Gas Rate, SCF/D

Water Rate, BBL/D


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d. Plotting Water Gas Ratio Method 15 CBM wells

Colorado's Field in Figure 23 is the actual production

profile of the well no 15 from the field in the “Z” Basin,

CBM Field, Colorado Field, where it gas-water rate will

be predictable.

Figure 21 Trendline water rate vs. time from 11 wells Colorado.

Figure 22 Predicted behavior of CBM production wells 11 Colorado.

In Figure 24 is a plot between GWR vs. Time, wells no 11

from the field in the “Z” Basin, CBM Well, Colorado Field

which produces a linear line.

Figure 23 Profiles 15 CBM production wells.

In Figure 25 is a plot between the Water Rate vs. Time, wells

no 15 from the field in The “Z” Basin, CBM Field, Colorado

Field which produces a linear line on a logarithmic scale.



Rate Prediction results of the proposed methods wells no 15

from the field in “Z” Basin, CBM Field, Colorado Field the

GWR - Water Rate Plotting.

y = -54.55x + 24450R² = 0.165

0

50,000

100,000

150,000

200,000

250,000

1400 1500 1600 1700 1800 1900 2000 2100 2200

GA

S W

AT

ER

R

AT

IO

TIME, DAYS

TIME vs GAS WATER RATIO

Gas-Water Ratio

Linear Regression

y = -1.57ln(x) + 16.83R² = 0.022

0

2

4

6

8

10

12

1200 1600 2000 2400

WA

TE

R

RA

TE

, B

BL/D

TIME , DAYS

TIME vs WATER RATE

Water Rate, BBL/D

0

100

200

300

400

500

600

700

800

900

1,000

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

4/19/01 9/1/02 1/14/04 5/28/05 10/10/06 2/22/08 7/6/09

WA

TE

R R

AT

E, B

BL/D

GA

S R

AT

E, S

CF

/D

DATE


Gas Rate, SCF/D

Water Rate, BBL/D

y = -14.08x + 87321R² = 0.001

0

50,000

100,000

150,000

200,000

250,000

300,000

2400 2500 2600 2700 2800 2900 3000

GA

S W

AT

ER

RA

TIO

TIME, DAYS

TRENDLINE OF GAS WATER RATIO

Gas-Water Ratio

Linear Regression


ISSN: 2278-0181




1158

Figure 25 Trendline water rate vs. time from 15 wells Colorado.

Figure 26 Predicted Behavior 15 CBM production wells Colorado.

a. Water Gas Ratio Data Plotting Method Using Fekette.

In Figure 27 is the actual production profile using the data

Fekette, which his gas-water rate will be predictable.

Increase in gas flow rate on the picture is as a result of

stimulation using hydraulic fracturing stimulation process

which is commonly done on CBM reservoir to be able to

increase the production of which has been dropped

because of the possibility of cleats that are sensitive

permeability resulting in decreased permeability cleats.

Figure 27 Profile use a data Fekette CBM production.

In the figure 28 is a plot between GWR vs. Time, using

Fekette's data, which produces a linear line.

Figure 28 Trendline of GWR using the data Fekette.

In Figure 29 is a plot between the Water Rate vs. Time,

using the data Fekette, which produces a linear line on a

logarithmic scale.

Figure 29 Trendline from the water using a data rate Fekette.

y = -8.76ln(x) + 84.35R² = 0.145

0

10

20

30

40

50

1000 1500 2000 2500 3000 3500

WA

TE

R

RA

TE

, B

BL/D

TIME, DAYS

TRENDLINE OF WATER RATE

Water Rate, BBL/D


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1159


Rate Prediction results of the proposed method using the

data Fekette GWR - Water Rate Plotting.

From the plot between GWR against time previously

observed, it appears that slope or slope GWR magnitude

varies with time. This likelihood is a function of reservoir

characteristics such as porosity, permeability, gas

content and well spacing.

Figure 30 Predicted behavior using the data Fekette CBM production.

Further research is recommended to observe it in depth, so as

to estimate the characteristics of CBM reservoirs by

inclination or slope of the time the GWR.

II.DISCUSSION

For this method, the concept of the idea is the actual

production function is a function of time, which can be

obtained by a linear relationship. So it can be used to predict

the gas-water rate in the future, after the CBM wells or

CBM reservoir is undergoing production mature level.

Have investigated several relationships, among others:

Cum GWR vs. Time, Cum GWR and GWR vs vs Gp Gp.

The results show a good consistency, as a reference in the

production of CBM is forecasting relationship Water Gas

and Water Rate Ratio Plotting. In this study the proposed

prediction method is fast, practical and reasonably accurate

based on Water Gas and Water Rate Ratio Plotting. The

purpose of these studies to predict the future behavior of

CBM production through production data correlation in

order to obtain a linear regression equation. This method

was applied to some actual data as follows:

a. Synthetic Data (From Reservoir Simulation CBM).

b. Data Pilot Project of India.

c. Data from The “Z” Basin, CBM Field.

d. Data from Fekette

From testing with the above data, Production Ratio Method

Plotting give good results to predict the rate of gas

production and for water rate, yield and Late Initial Water

Rate Time Rate her very fit. In other words, the predicted

results with this method, has been compared with the

simulation, and the results of Gas Rate and Long Life Time

fit. This method is very handy and quick use. Limitation of

this method is:

- The more data, the better the outcome will be.

- Gas production rate has reached a maximum (the

beginning of the decline in production).

III. CONCLUSION

1. Single Well Model Based Simulation, GWR gained versus

Time straight line while the QW vs Time obtained a

straight line on a logarithmic scale. Based on the two

plotting this function, can be obtained CBM production

forecasting. The results of this method have been

compared with simulation results, and obtained

comparable results.

2. Data pilot project (India) showed that GWR versus Time

gives a straight line, while the QW vs Time obtained a

straight line on a logarithmic scale. By plotting these

functions, can be obtained CBM production forecasting.

3. From production data on the wells in the “Z” Field, GWR

gained versus Time straight line while the QW vs Time

obtained a straight line on a scale logarithmic. Based on

the two plotting this function, can be obtained CBM

production forecasting.

4. Operational changes during CBM production (data from

Fekette) showed that GWR versus Time, Ʃ GWR versus

Time and Gp/ Wp versus Time shows a straight line. By

plotting these functions have been compiled CBM

production forecasting methods.

5. Advantage of this method is the GWR plotting function

we can simultaneously predict CBM production and

integrated water. In the previous methods, such as decline

curve analysis, forecasting gas and water carried

separately.

6. GWR slope of the time is a function of reservoir

characteristics such as porosity, permeability, gas

content and well spacing. Further research is

recommended to observe it in depth, so as to

estimate the characteristics of CBM reservoirs by the

slope of the time the GWR.

IV. REFERENCE [1] Abdassah, D. (2007): Lecture CBM Technology, Institut Teknologi

Bandung.

[2] Ershaghi, I., and Abdassah, D. (1984): A Prediction Technique for

immiscible Processes Using Field Performance Data, Journal of

Petroleum Technology, 664-70.

[3] Ershaghi, I,. and Omoregie, O. (1978): A Method for extrapolation of

Cut vs. Recovery Curves, Journal of Petroleum Technology, 4,203.

[4] Ertekin, Turgay. (2005): Coal Seams as a Natural Gas Reservoirs, SPE

Workshop, Bandung Institute of Technology.

[5] Irawan, Dedy. (2007): Method Behavior Evaluation and Forecasting

Production For Applications in Field-Old Course (Brown Fields),

Thesis, Bandung Institute of Technology.

[6] King, G.R. (1990): Material Balance Techniques for Coal Seam

and Devonian Shale Gas Reservoirs, Paper SPE 20730, presented at the

65th SPE Annual Technical Conference and Exhibition, New Orleans,

Louisiana.


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[7] Mavor, M.J., and Robinson, J.R. (1991): Western Cretacceous Coal

Seam Project, Quarterly Review of Methane from Coal Seam

Technology 8, 4.

[8] Mavor, M.J., and Robinson, J.R. (1993): Analysis of Coal Gas Reservoir

Interference and Cavity Well, Paper SPE 25 860.

[9] Okeke, A.N. (2005): Sensitivity Analysis of Modeling Parameters that

Affect the Dual Peaking Behavior in Coalbed Methane Reservoirs, MS

Thesis, Texas A & M University.

[10] Stevens, S.H., and Sani, K. (2002): Coalbed Methane Potential of

Indonesia: Preliminary Evaluation of a New Natural Gas Source,

Proceedings, Indonesian Petroleum Association, Twenty-Eight Annual

Convention & Exhibition.


ISSN: 2278-0181




1161

based on actual production data (gas water ratio plotting) · 1. synthetic data of cbm reservoir...

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