based on actual production data (gas water ratio plotting) · 1. synthetic data of cbm reservoir...
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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
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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
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14
16
18
0
1,000
2,000
3,000
4,000
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6,000
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8,000
9,000
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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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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
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500
600
0
50,000
100,000
150,000
200,000
250,000
300,000
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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
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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
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WA
TE
R R
AT
E, B
BL/D
GA
S R
AT
E, S
CF
/D
DATE
CBM PRODUCTION PROFIL - Well 11
Gas Rate, SCF/D
Water Rate, BBL/D
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Figure 20 Trendline from water gas ratio vs. time 11 wells Colorado.
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.
Figure 24 Trendline from water gas ratio vs. time 15 wells Colorado.
In Figure 26, a production history followed by Gas-Water
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
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WA
TE
R R
AT
E, B
BL/D
GA
S R
AT
E, S
CF
/D
DATE
CBM PRODUCTION PROFIL - Well 15
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
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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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In Figure 30, a production history followed by Gas-Water
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
charac- teristics 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.
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[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,
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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
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[8] Mavor, M.J., and Robinson, J.R. (1993): Analysis of Coal Gas Reservoir
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[9] Okeke, A.N. (2005): Sensitivity Analysis of Modeling Parameters that
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