Production Performance Prediction in Coalbed Methane Reservoir

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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

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 Fields 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.

  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

    CBM PRODUCTION PROFIL

    9,000

    18

    8,000

    Gas Rate SC

    Water Rate SC

    16

    7,000

    14

    6,000

    12

    5,000

    10

    1,000

    2

    0

    10/10/2006

    22/09/2017 04/09/2028 18/08/2039 31/07/2050

    0

    13/07/2061

    DATE

    4

    2,000

    6

    3,000

    8

    4,000

    GAS RATE, SCF/D

    WATER RATE, BBL/D

    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

    300,000

    CBM PRODUCTION PROFIL

    PRODUCTION HISTORY

    600

    200,000

    400

    150,000

    Assumed Abandonment Rate

    Qgas = 27.4 MSCF/D

    300

    100,000

    200

    50,000

    GAS RATE, SCF/D

    100

    6/1/2031 8/18/2039 11/4/2047 1/21/2056

    DATE

    WATER RATE, BBL/D

    0

    10/10/2006 12/27/2014 3/15/2023

    PREDICTION

    GAS RATE PREDICTION

    250,000 500

    IGIP = 2.36 BSCFD GP = 1.75 BSCFD RF = 74.2 %

    GAS RATE, SCF/D

    WATER RATE, BBL/D

    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 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.

    900,000

    800,000

    700,000

    Gas Rate, scfd

    600,000

    500,000

    400,000

    300,000

    200,000

    100,000

    CBM Well Production

    Gas Rate, scfd Water Rate, bw pd

    90

    80

    70

    Water rate, bwpd

    60

    50

    40

    30

    20

    10

    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 WR – Water Rate Plotting.

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

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

    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.

There are 4 pieces CBM wells into data from Water Gas Ratio Method in the field plotting Z, Colorado.

  1. 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.

    3,000,000

    CBM PRODUCTION PROFIL – Well 7

    3,000

    Gas Rate, SCF/D

    300

    250

    WATER RATE, BBL/D

    200

    150

    100

    50

    0

    Water Rate, BBL/D

    y = -90.9ln(x) + 928.3

    Logarithmic Regression

    R² = 0.012

    TIME vs WATER RATE

    1,000

    1,000,000

    1,500

    1,500,000

    2,000

    2,000,000

    2,500

    Water Rate, BBL/D

    2,500,000

    GAS RATE, SCF/D

    WATER RATE, BBL/D

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

    500,000

    500

    0

    8/11/87

    0

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

    DATE

    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.

    TIME, DAYS

    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.

  2. 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.

  3. 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.

  4. 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.

    180

    160

    140

    120

    100

    80

    60

    40

    20

    0

    Water Rate, BBL/D

    CBM PRODUCTION FORCAST – Well 19

    2,000,000 200

    GAS RATE SCF/D

    WATER RATE, BBL/D

    from the field in the Z Basin, CBM Field, Colorado Field, where his gas-water rate will be predictable.

    1,800,000

    1,600,000

    1,400,000

    1,200,000

    1,000,000

    800,000

    600,000

    400,000

    200,000

    0

    Gas Rate, SCF/D

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

    DATE

    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

    TIME vs GAS WATER RATIO – Well 19

    y = -4.508x + 71903

    R² = 0.014

    Gas-Water Ratio

    Linear Regression

    400,000

    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.

    CBM PRODUCTION PROFIL – Well 11

    350,000

    GAS WATER RATIO

    300,000

    250,000

    200,000

    1,200,000

    1,000,000

    GAS RATE, SCF/D

    800,000

    200

    Gas Rate, SCF/D

    Water Rate, BBL/D

    180

    160

    WATER RATE, BBL/D

    140

    120

    150,000

    100,000

    50,000

    0

    0 500 1000 1500 2000 2500 3000 3500

    TIME, DAYS

    600,000

    400,000

    200,000

    0

    100

    80

    60

    40

    20

    0

    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.

  5. Plotting Water Gas Ratio Method 11 CBM wells Colorado's Field In Figure 19 is the actual production profile of the well no 11

TIME vs WATER RATE – Well 19

y = -4.45ln(x) + 43.59

Water Rate, BBL/D

R² = 0.146

60

WATER RATE, BBL/D

50

40

30

20

10

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

DATE

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

0 500 1000 1500 2000 2500 3000 3500

TIME, DAYS

Figure 17 Water rate vs. time 19 wells Colorado.

TIME vs GAS WATER RATIO

250,000

Gas-Water Ratio

Linear Regression

200,000

150,000

100,000

50,000 y = -54.55x + 24450

R² = 0.165

0

1400 1500 1600 1700 1800 1900 2000 2100 2200

TIME, DAYS

which produces a linear line.

GAS WATER RATIO

Figure 20 Trendline from water gas ratio vs. time 11 wells Colorado.

2,500,000

2,000,000

GAS RATE, SCF/D

1,500,000

1,000,000

500,000

0

CBM PRODUCTION PROFIL – Well 15

Gas Rate, SCF/D Water Rate, BBL/D

1,000

900

800

700

WATER RATE, BBL/D

600

500

400

300

200

100

0

  1. 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.

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

    DATE

    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.

    TIME vs WATER RATE

    12

    Water Rate, BBL/D

    y = -1.57l(x) + 16.83

    R² = 0.022

    300,000

    10

    8

    6

    4

    2

    0

    1200

    WATER RATE, BBL/D

    250,000

    GAS WATER RATIO

    200,000

    150,000

    100,000

    50,000

    TRENDLINE OF GAS WATER RATIO

    1600

    TIME , DAYS

    2000

    2400

    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

    0

    Gas-Water Ratio

    y = -14.08x + 87321

    R² = 0.001

    Linear Regression

    2400 2500 2600 2700 2800 2900 3000

    TIME, DAYS

    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.

    TRENDLINE OF WATER RATE

    Water Rate, BBL/D

    y = -8.76ln(x) + 84.35

    R² = 0.145

    50

    40

    WATER RATE, BBL/D

    30

    20

    10

    0

    1000 1500 2000 2500 3000 3500

    TIME, DAYS

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

    Figure 26 Predicted Behavior 15 CBM production wells Colorado.

    1. 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.

      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.

      1. 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:

        1. Synthetic Data (From Reservoir Simulation CBM).

        2. Data Pilot Project of India.

        3. Data from The Z Basin, CBM Field.

        4. 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).

      2. 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, 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.

      3. 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.

  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 o 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.

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