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    Computers

    & Chemical

    Computers and Chemical Engineering 24 (2000) 453-456

    Engineering

    www.elsevier.com/locate/compchemeng

    Gas pipeline leak detection system using the online simulation

    method

    Kenya Fukushima a~*, Reiko Maeshima b, Akira Kinoshita b, Hitoshi Shiraishi b,

    Ichiro Koshijima c

    a

    Development D epartment IV, Facili ti es Development D iv ision, Japan Petrol eum Explorat ion Company Ltd., Z-2-20 Hi gashi-Shinagawa,

    Shinagawa- ku, Toky o, Japan

    b Chiyoda Corporation, 2-12-1 Tsurumi-chuo, Tsurumi-ku, Yokohama, Japan

    c Depart ment of Proj ect M anagement, Chiba Inst it ut e of Technol ogy, 2-17-1 Tsudanuma, Narashi no, Chi ba 2750016, Japan

    Abstract

    Management of natural gas pipeline is an important task for economical and safety operation, loss prevention and

    environmental protection from methane emission.

    A

    leak detection of gas pipeline, therefore, plays a key role in the overall

    integrity management for a pipeline system. Especially for a long pipeline operated alongside of densely populated areas, a leak

    detection system is an indispensable condition to allow its construction. In this paper, a leak detection method based on a dynamic

    simulation with wave equations is presented. An industrial application to one of the longest gas pipeline is also presented with its

    performance information. 0 2000 Elsevier Science Ltd. All rights reserved.

    Keywords: Gas pipeline; Simulation method; Methane

    1. Introduction 2. Overview of Niigata Sendai pipeline

    Natural gas becomes a major energy resource

    in Japan,

    because of its cleanness and high unit-calorie. In order to

    utilize natural gas, logistics is the most important parts

    after we constructed liquefaction plants and receiving

    terminals. Gas pipeline is a most efficient logistics to deliver

    natural gas. However, serious problems related not only to

    environment but also economy will be caused if pipeline

    happens to leak at any point. Accordingly, a leak detection

    of gas pipeline plays a key role in the overall integrity

    management for a pipeline system.

    Niigata-Sendai pipeline is one of the longest natural gas

    pipeline in Japan with the length of about 250 km from

    Niigata to Sendai of the Main Island. This pipeline has the

    maximum capacity of 4 500 000 Sm3/day and includes 30

    valve stations located in mountainous and snowy districts

    (over 2 m of snow in winter). Open rack vaporizer (ORV)

    vaporizes native LNG as the source of this pipeline for

    many users, particularly for a power plant (600000 kW)

    located at Sendai area (population of 1000 000).

    A lot of technologies have been reported for leak

    detection with various applicability and restrictions. Tech-

    nologies currently available include the following method,

    such as the volume-mass balance method, the pressure

    monitoring method with statistical analysis and/or pattern

    matching and acoustic monitoring method. The attain-

    ment of most reliable leak detection system for a long

    pipeline in a densely populated district such as Japan is a

    great challenge to the pipeline provider.

    In the northeastern region of Japan, comparing with

    other regions of Japan, natural gas has not spread so much

    because of late infrastructure in spite of many advanta-

    geous properties particularly in the field of environmental

    protection. In this respect, it has been expected that

    Niigata-Sendai pipeline initiates to make the natural gas

    more widespread in this area and makes noted contribu-

    tions to the prosperity and activation of this local society

    along this pipeline.

    In this paper, we would like to represent our installed

    leak detection system and its performance using real

    operational data on our Niigata-Sendai pipeline.

    3.

    Proposed leak detection method

    * Corresponding author.

    3.1. Condition of detection

    The leak detection is actually performed by measuring

    pressure and temperature at the valve stations, which are

    0098-1354/00/ - see front matter 0 2000 Elsevier Science Ltd. All rights reserved.

    PII: SOO98-1354(00)00442-7

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    K. Fukushima et al. Comput ers and Chemi cal Engin eering 24 (2000) 453-456

    0

    Et )$I+,

    . 1

    0

    C:df$=+E

    l&4 l&J

    __+-_-+B=O

    Adt Bdt

    C-: ,=-B

    Fig. 1. Solving of wave equations for pipeline model.

    spaced

    each other with the average distance of 12 km, and

    by measuring flow rates of gas at only inlet and outlet of

    this pipeline. It should be noted that since the power plant

    of Niigata-Sendai pipeline is operated on demand basis,

    there is no steady state in its operation. Under the compa-

    nys safety policy dictated, the leak detection system has

    to automatically determine a leaking point and its leaking

    rate in real-time basis.

    3.2. Basis of leak detection method

    Our proposed method is an extension of the volume-

    mass balance method (Modisatte, 1984; Blackadar &

    Massion, 1987). In this method, the mass balance is

    modeled along the pipeline. The simulation model for the

    pipeline is based on a transient flow model shown by the

    following equations.

    Continuity Eq.:

    (1)

    4

    -rt

    h

    t

    up&$

    w

    Potential Leakage atVS Potential Leakage at VS,,

    =

    M i-1, n) - M i, l} = M i, n) - M i+l , 1)

    = should be 0

    = should be 0

    h

    b)

    h

    d

    1

    h44 A.fJpJ

    Potential Leakage at VS, Potential Leakage at VS.,,,

    =

    M i-I,n) - M i,1) = M i, n) - M i+l, 7)

    > Threshold > Threshold

    Fig. 2. Prediction of leakage. (a) Mass balance in case of no leakage.

    (b) Mass balance in case of leakage.

    I I

    1Leak

    Detecfi na M odule

    IPotentialeakage Leakage

    LW

    Fig. 3. Leak detection system for Niigata-Sendai pipeline.

    Momentum Eq.:

    pv apv2

    p

    7

    t

    +t++pgsina+g=O

    (2)

    Fig. 4. Examples of use IF.

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    K. Fukushima et al. Comput ers and Chemi cal Engineeri ng 24 (2000) 453-456

    455

    Fig. 5. Verification of flow sensors.

    3500 I

    3ocQ

    2500

    zooo

    1500

    1000

    500

    0

    3

    6 9 2 5 6 al 26

    a) Typical DemandChanges 24br)

    68.0

    97. 0

    39. 0

    95. 0

    94. 0

    63. 0

    62. 0

    91. 0

    80. 0

    59. 0

    58.0 1

    0 3 6 9 12 15 19 21 24

    b) Typical PressureProfiles at Valve Stations(24hr)

    Fig. 6. Sample of actual operation data. (a) Typical demand changes

    (24 h). (b) Typical pressure profiles at valve stations (24 h).

    State Eq.:

    P

    -=zRT

    P

    3)

    These partial differential equations are solved on Gen-

    sym G2. Though G2 does not have any numerical solver

    for partial differential equations, G2 has to be a core engine

    to manage whole system including network communica-

    tion and real-time SCADA. On G2, coupled partial differ-

    ential equations are converted to ordinary differential equa-

    tions with the method of characteristics as shown in Fig. 1.

    3.3. Real-time detection method

    Leak detection is performed in two steps. In the first step,

    a pressure-balance of the pipeline is simulated based on the

    above model. In the second step, mismatch between simu-

    lated and actual pressure-balance is evaluated to determine

    a possible leakage.

    ._.

    -vs6

    - v616

    _

    -vs?l

    A r Tuning --vs?o

    Fig. 7. Effect of parameter tuning.

    3.3.1. First step

    Two dependent variables of pressure and mass-flow are

    estimated on the basis of the known pressure-balance and

    flow rates collected through SCADA. It can be assumed

    that the potential leakage is defined by mismatch of mass-

    balance at each valve station. In the moment when we can

    assure that there is no actual leakage, a parameter tuning

    is performed for reconciliation of the pressure-balance to

    minimize the potential leakage in each valve station.

    3.3.2. Second step

    Theoretically speaking, if we can develop a high fidelity

    pipeline model, the potential leakage may be negligible

    small. In actual condition, there are various uncertainties

    that affect the estimation of pressure-balance, and a poten-

    tial leakage has a certain value.

    The leak detection, therefore, can be conducted by

    comparing the potential leakage and preliminary defined

    threshold in each valve station. If the potential leakage is

    larger than the threshold, it can be detected as a possible

    leakage as shown in Fig. 2. The location of leakage is also

    predicted between two points whose thresholds are

    violated.

    4. Leak detection system

    The implemented leak detection is illustrated in Fig. 3.

    In this system, there are six modules.

    4.1. SCADA I/F module

    This module communicates with SCADA through Eth-

    ernet. Through this I/F, operation data of each valve

    station are transferred from SCADA every 30 s.

    4.2.

    Tuning module

    Data reconciliation for parameter tuning is performed

    every 30 min. Tuned parameters are fed into the leak

    detection module. Error in reconciliation is also fed to the

    threshold module.

    4.3. Leak detection module

    Using tuned parameters and actual data, transient flow

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    K. Fukushima et al. /Comput ers and Chemi cal Engineeri ng 24 (2000) 453-456

    simulation is performed every 5 s. Incidentally, as the actual

    data are received every 30 s, received data are interpolated

    to match the calculation interval (5 s). Comparing with

    simulated results and thresholds, location of leakage and

    rate of leakage are predicted.

    4.4. Use I/F module

    As shown in Fig. 4, this module provides a graphical user

    interface, which includes geographical information, re-

    ceived field data readouts with trend graphs and warning

    messages that shows a possible leakage with related infor-

    mation.

    5. Actual operation

    5.1. Problems duri ng implementat ion

    During the implementation of this detection system,

    problems listed below may be caused.

    5.1.

    I.

    Unreli able data acquisit ion

    Unreliable data are acquired due to large instrument

    error on the inlet and outlet flow sensors as shown in Fig.

    5. At the inlet, averaged deviation of flow rate ( = measured

    flow rate/estimated flow rate) was 1.042. At the outlet,

    averaged deviation of flow rate was 1.989. Accordingly, it

    can be assessed that the inlet flow sensor may have an

    instrument error. This instrument error could not reveal

    without this leak detection system that calculates a dy-

    namic mass-balance even during transient conditions.

    51.2. Unexpected l ow resolut i on of pressure sensor

    As for the resolution of pressure sensor in this system,

    the designed rating was 14 bit, while the actual value was

    12 bit. This directly affects the prediction accuracy. In

    order to overcome such low resolution of pressure sensor,

    the length of each segment is optimized so as to validate

    pressure difference between both ends of the segment. We

    optimized the length of each segment to validate pressure

    difference between both ends of the segment from 23

    segments to 4 segments. Even when we reduced the number

    of segments, the system still shows a sufficient performance

    described in Section 5.2.

    51.3.

    Uncontroll ed fl uctuati on

    Operation mode of ORVs sometimes must be changed

    according to the users demand. For example, daily de-

    mand changes and associated pressure profiles in each

    valve station are shown in Fig. 6a and b. In an actual

    situation, large demand changes cause a reverse flow in the

    downstream of the pipeline. (The pressue of VS29 is higher

    than the pressure of VS15 and VS21 from 22:00 to 9:00 h,

    where VS29 is located at the downstream of VS15 and

    VS21). However, such change often caused uncontrolled

    fluctuations in this detection system and created false

    alarms. In order to suppress such false alarms and distin-

    guish real faults, the threshold is automatically tuned by

    forecasting the changes with an on-line learning capability.

    5.2. Expected performance of l eak det ecti on

    In this system, the performance of leak detection is based

    on the parameter tuning. Fig. 7 shows a typical trend of

    potential leakage at each valve station. The upper graph

    shows the trend before tuning, and the lower shows the

    trend after tuning. It is clearly understood that the parame-

    ter tuning effectively compensates uncertainties even in a

    transient situation and supports a reliable leak dete-

    cion.This leak detection system is to be operated under the

    following expected performances based on leak tests and

    simulations.

    52.1. M ini mum l eakage rat e

    The minimum leakage rate for detecting the leakage is

    l.l%, which is equivalent to a leakage from the diameter

    of 0.9 cm hole. The minimum leakage rate for estimating

    the leakage point is 1.8% (equivalent to a diameter of 1.1

    cm hole).

    5.2.2. Average detecting time

    The average detecting time under the maximum load is

    8 min, and under the minimum load is 4 min. On the other

    hand, the average detecting time under the largest transient

    situation is 4 min.

    5.2.3.

    Av erage l ocati on error

    The average location error under the maximum load is

    4% and under the minimum load is 20%. The average

    location error under the largest transient situation is 6%.

    6. Concluding remarks

    A leak detection framework based on a wave equation

    has been presented. It is clear from the foregoing that the

    presented leak detection method was successfully imple-

    mented to one of the longest gas pipelines in Japan. Since

    1997, this leak detection system has been inspecting leakage

    troubles. Fortunately there are no signs of leakage to

    validate its real performance. It is believed that the user of

    this system prevents serious troubles and increases integrity

    of the pipeline system.

    Acknowledgements

    The authors wish to express sincere appreciation to

    Japan Petroleum Exploration Co. Ltd. and Chiyoda Cor-

    poration for supporting the present study and for permit-

    ting the publication of this paper.

    References

    Modisatte, J. L. (1984). A comparison of transient pipeline flow models

    and features,

    PSIG Annual M eeti ng,

    Chattanooga, TN, October

    l&19, 1984.

    Blackadar, D. C., & Massion, R. V. J. (1987). Implementation of a

    real-time transient model for a batched pipeline network, PSIG Annual

    M eeti ng, Tulsa, OK, October 20-23, 1987.