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Evaluation of a gas pipeline simulation program
Mathl. Comput. Model&g Vol. 15, No. 7, pp. 1-14, Printed in Great Britain. All rights reserved
EVALUATION
Copyright@
OF A GAS PIPELINE W. J. TURNER,
CSlBO
1991
P.
S-J.
KWON
0395-7177/91 $3.00 + 0.00 1991 Perg8mon Press plc
SIMULATION AND P.
Division of Mineral and Process Engineering,
A.
PROGRAM
MAGUIRE*
Lucas Heights Research Laboratories
Private Mail Bag 7, Menai 2234, New South Wales, Australia (Received July 1989 and in revised form
October 1990)
Abstract-As with any predictive scientific tool, the best test of 8 simulation progr8m is to compare its simulated results with values from the real world. A 7-day set of SCADA measurements from a transportation pipeline network extending over 1500 km is used for this purpose. The results of the comparisons with 8 gas pipeline simulation program, SIROGAS, are presented. The objective was to observe and possibly improve its suitability for use as an on-line leak detection tool by testing the capability of the progr8m to simulate 8 large network. The method ~8s to set some of the SCADA data as boundary conditions and compare the remainder with the simulation results. The results 8re quantified through the means and statistical confidence intervals for differences between measured and simulated values of the major variables. The results sometimes indicated abnormalities in the network instrumentation. The performance of the compressor model is discussed and 8 mechanism is suggested for smooth simulations while receiving temporarily erroneous SCADA readings. The effects of boundary condition configurations on simulation 8re also discussed.
1. INTRODUCTION Many differing and ingenious methods have been published in the literature for detection, location, and sizing of leaks in gas pipelines [l-4]. It is generally agreed that transient simulation programs offer one of the more complete solutions to the problem of leak detection [3,5-lo]. Butler [ll] articulated four requirements of an ideal leak detection system; sensitivity, accuracy of leak location, fast response time, and absence of false alarms. The challenge for current simulation/leak detection programs is to satisfactorily meet these requirements in realistic networks in which compressors start and stop from time to time, and such large pressure changes occur that the nonlinear governing equations must be used. there must be a non-trivial leak detection mechanism To meet the above requirements, (e.g., [9,10]), coupled with a suitable simulation program which can closely monitor the operation of a network. Seemingly, the best way to test the suitability of a program is to compare the simulation results to the measured data from an existing pipeline network. Such data are readily available through the Supervisory Control And Data Acquisition (SCADA) systems that are at present integral parts of many pipeline networks. There have been only a few papers on this type of comparisons, seemingly supporting the declaration of Watanabe and Himmelblau [4] that “these methods are quite interesting but are of purely theoretical nature, validated only by computer simulations or by simplified experiments.” One such paper was based on a 80 km section of the British Gas network, used by Chapman, Jones, and Pritchard [12] to test their transient program. They were for a simulation period of 4 h at a sampling interval of 10 min and another at 45 and 3 min respectively. Their study was on a single length of pipe with an off-take valve located halfway along the pipe. It consisted mainly of simulations of a steady state pressure profile, a linearly decreasing pressure versus time profile, and a pressure versus time profile for a short period after an opening of a valve. *The SCADA data were provided by The Pipeline Authority (TPA) of Australia. Discussions with personnel of the TPA, especially Mike Severs, were of considerable aid in the work. Thanks 8re 8lso due to Leeds 8nd Northrup of Australia and Rosemount Instruments for their contribution. Typeset by A&-‘QX I
W.J.
2
TURNER et al.
A larger and more rigourous test was applied by Lappus and Schmidt [13] to their isothermal model of a 75 km long section of a gas transportation network. The simulation was performed for a six-hour set of measurements available from the SCADA system at five-minute intervals. Their main conclusion was similar to the above study in that it was claimed that after a reasonable initial settling-in period, the simulated values converged to the measured values in all cases. Again, the network studied was a simplified set-up in that no devices such as the compressors were present and the length of the pipeline and the duration of the simulations were short. The comparisons presented in this paper are of a more testing nature than those stated above. The simulated pipeline network is larger, has a two unit compressor station and several side streams. The transients are induced by the irregular and cyclic pressure and flow variations as read from the SCADA system. The lengthy interval over which the simulation was performed ensures that relatively thorough tests were made on the program’s ability to simulate many of the situations possible in gas pipeline operations. The ability to simulate the compressor station was also tested in that only the compressor discharge pressure was input to the simulation. 2. THE
PROGRAM
(SIROGAS)
SIROGAS is a FORTRAN 77 program for simulating steady state and transient behaviour of gas flowing in pipeline networks [14,15]. The program considers a network to consist of two entities: pipes and connections. The fluid flow in each pipe is modelled by the partial differential equations for one dimensional gas flow [16]. The three equations are (a) conservation of mass, (b) conservation of momentum, and (c) conservation of energy. All terms are included in the equations; even kinetic and gravitational energy. The pipes are joined by connections representing a wide range of hydraulic components. In SIROGAS, these may be pipe junctions, demand or supply points, compressors, non-return valves, regulators, pipe breaks, tanks, and other components. The detailed compressor model includes recycle, a cooler, and station pressure loss. The linearised implicit finite-difference equations for the pipes and all connection equations except the sonic flow equation are solved by direct solution procedures. SIROGAS includes the capability to compute the variation in gas composition in both space and time, and its effect on the gas flow. Hence the evaluation of the thermodynamic properties of the gas mixture at each node and time step is an important part of SIROGAS. Starling’s generalized equation of state [17] for light petroleum mixtures is used. This general equation contains several coefficients which are functions of gas composition. The relevant values of the coefficients are used to calculate the gas temperature, density, entropy, and other parameters from the pressure and enthalpy values available at every finite difference node. Starling’s published procedures for solving his equations are not used; instead a highly efficient procedure has been developed which converges in one or two iterations in almost all cases. The program is used extensively throughout Australia for design and case study applications and is being installed as a real time model in the new SCADA system for the AGL network in Eastern Australia. 3. THE
SIMULATION
The gas pipeline network studied (Fig. 1) is operated by The Pipeline Authority (TPA) of Australia who are responsible for the transportation of natural gas from Moomba in central Australia to the Wilton interchange near the East coast. All of the natural gas supplied to the Sydney metropolitan area and the industrial centres of Newcastle and Wollongong passes through this interchange. TPA is also responsible for the supply of natural gas to Canberra, the capital city of Australia. The network extends over 1500 km and there is a two-unit compressor station located midway at Bulla Park, 578 km downstream from Moomba. The compressors used are Dresser Clark DC-990 model 553P2. Since this study a second two unit station has been installed at Young 1003 km downstream from Moomba.
Gas pipeline simulation program
3
Fig. 1. TPA network.
Flows are measured at all demand and supply points by DP devices (6 measurements). Pressures and temperatures are measured at these points and at all main line valve sites by diaphragm pressure meters and platinum RTD probes, respectively (31 measurements each). Typical operating pressures (around 6 MPa) and temperatures (around 20 “C) are as shown in Figs. 5a and 18a. Typical flows at the main supply and demand points are shown in Figs. 8 and 12a, respectively. Flows of 50 kg/ s correspond to 250,000 standard M3/h. The SCADA data supplied by TPA covered over 165 hours from Monday the 8th to Sunday the 14th of June 1987 at one minute intervals. The readings were taken from the historical files written routinely by the Leeds and Northrup (Australia) SCADA system. Hence the simulation program received exactly the same data as would a real time model for leak detection. The Leeds and Northrup system transmits all SCADA readings with a numeric flag indicating the status of the reading. This enables the program to take action based on this flag. Throughout this simulation, the action was to take the value of the last correct reading if the status of the current reading is unsatisfactory. Figure 2 shows how the TPA pipeline network is represented as a SIROGAS network of pipes, connections, and finite difference nodes. The number of nodes in a pipe is roughly proportional to the length of the pipe. The compressor station at Bulla Park is represented as two connections, one at the suction and the other at the discharge side. This is a consequence of the way in which SIROGAS simulates gas-powered compressors. Compressors in SIROGAS can operate in several modes; in this simulation discharge pressure control mode was used.
Fig. 2. Schematic representation of a TPA network.
W.J. TURNER et al.
4
SIROGAS allows either pressure or flow controlled boundary conditions. After some experimentation, we took flow-control at all boundary condition points except at the main demand point (Wilton). This was later found to be not an ideal configuration. The reasons will be discussed in Section 4.1.2 where we will show why some boundary condition configurations are to be preferred over others. The main reason for choosing the above configuration was the presence of pressure noise at Moomba, presumed to be from the centrifugal compressor in the Moomba gas plant. The compressor station was simulated with the SIROGAS 2 unit compressor station including the complete manufacturers performance chart for both angular velocity and efficiency. Both suction and discharge pressures and temperatures were available from the SCADA, but only the discharge pressure was used as input to the model; the suction pressure, and the two temperatures being calculated by the model. The fuel usage and the compressor heating were both found to have a significant effect and were calculated by the model. The amount of data from the SCADA system was insufficient to define its dynamic state. Hence the initial conditions were acquired by running a steady-state calculation of the network using some of the SCADA variables as boundary conditions. At the start of the simulation, there were discrepancies between the simulated values and some of the SCADA values not used as boundary conditions. As expected, some settling period was required before the simulated values began to track the SCADA values. 3.1.
Thermal
Model
The main difficulty in testing the thermal model of programs such as SIROGAS is that there are not enough thermal data available from the SCADA systems. A good thermal model needs to take account of the ground temperatures along the pipeline as well as the gas temperatures. The pipe wall temperature readings would also be a bonus. The ambient temperature readings are essential for instances in which the pipes are above ground or when the gas is being aircooled, as in the compressor station site. But much of the above-mentioned data, except the gas temperatures and occasional ground and ambient temperatures, are not available from the normal SCADA systems. Therefore there are insufficient data to drive the thermal model and even less for comparisons. Two major assumptions were made to define the thermal model. First, a mechanism was developed to tune the ground temperature profiles. Within this mechanism, the ground temperatures were assumed to be a complex time-weighted function over 240 minutes of measured gas temperatures. The exact algorithm will not be discussed in this paper but this mechanism ensured that a realistic ground temperature profile was available and although the long-term comparison between the measured and calculated gas temperature could be biased, short-term comparisons were independent and valid. Note that within the network, gas temperature changes occur as a result of changes in altitude and location, as well as compressor station heating. Second, the non-SCADA, hourly readings of the ambient air temperature at the Bulla Park compressor site were available for the whole period. These were used in conjunction with the SCADA data to drive the compressor simulation; in particular the cooling of the discharge gas by forced air draught coolers. 4. RESULTS
AND ANALYSIS
Figures 3a, b, and c represent the mean of the differences between the simulated values of flow, pressure, and temperature from the corresponding measured values, respectively. Absolute values were used so that magnitude of the differences could be seen. The existence of the settling time is confirmed and it can be quantified at about 10 hours as seen from the pressure and flow plots. The plots show that the mean differences do not go beyond 5.0% for flow, 0.5% for pressure, and 0.5 ‘C for temperatures. Figures 4a and b show the means and the 95 and the 99% confidence intervals of the differences in pressures and temperatures. The confidence interval plots are based on the standard deviations of the differences calculated each time step. A comparision of flows is not presented, as there were insufficient measurement points.
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It should be recognized that pressure is the single most important variable to monitor. The pressure readings are more reliable than the flow readings and also more readily available than either the flow or temperature readings. They are also the basis of most leak detection packages predicted (e.g., [9]), along with th e fl ow readings at the supply and demand points. Accurately pressure values are vital for successful simulations. The pressure plots of Fig. 4a show a narrow band-width (at most 0.5%) and mean differences that stay around zero, except for the first 10 h, which as stated above is the initial settling period. The low mean values are of course not entirely accidental, the simulation had been designed and optimized with respect to the mean differences to give minimum overall sum by choosing the roughness values. This is called the roughness tuning. Another noticeable aspect of the plots are the gently sloping nature of the bands towards the negative region. This will be further discussed in Section 4.1.2.
W.J.
6
TURNER
et aI.
In the following sections site names will be frequently mentioned and they are listed in Table 1, together with the distances from Moomba and pipe diameter. To visualize the network, Table 1 should be read in conjunction with Figs. 1 and 2. Table 1. TPA network sites. Distance (km)
Site Booberoi
825
0.847
Bulla Park
578
0.847
Canberra Moomba young Wilton
4.1. Pressure
Diameter (m)
1142
0.266
0
0.847
1033
0.847
1298
0.847
Comparisons
Figures 5a, b, and c show the pressures versus distance along the main pipeline. The plots were taken at three different times to give the instantaneous views of the pressure profile. As the pipeline goes through the hilly inland regions of New South Wales and down the coastal hills to the flat estuarine regions of Wilton, there is a general drop as well as slight fluctuations in pressure. These are understandable in terms of the geography and also of the friction within the pipes. The slight increase in the pressure at the end of the pipeline is due to the drop in altitude near Wilton. The relatively large discrepancies between the measured and calculated pressures in Fig. 5a (at time 0) are due mainly to the effect of simulation starting from steady state as mentioned earlier. In Figs. 5b and c, the largest non-systematic discrepancies are of around 30 kPa-that is about 0.5% of the normal operating pressure.
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The only source of systematic discrepancy is at Booberoi. Initially we suspected the altitude value used in the simulation to be incorrect. Extensive checks revealed that while the altitude could be in error by up to 20 m this is well below the 50 to 60 m drop in altitude required to bring the comparison result into line. Instrumentation error is likely.
Gas pipeline simulation program
7
Moomba, Young, Canberra, and Bulla Park are the network supply point, a junction, a demand point, and the compressor site respectively. Figures 6a, b, 7a and b show the simulated and measured pressures at these sites; these comparisons give a good indication as to how the simulation has performed.
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Figures 6a and b show two excellent sets of results at Young and Canberra. The slight but consistent discrepancy at Young could be tuned out using the pipe roughness as a calibration factor (see Section 4.1.1). The results at Moomba and Bulla Park, shown in Figures 7a and b, were not so easy to interpret. Large discrepancies between the simulated and measured values occurred after 150 h.
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Figure 7
The problem was found to be due to the program getting incorrect SCADA readings for the flow at Moomba which is used as a boundary condition. The error in the flow reading at around 150 h can be seen from the Moomba flow comparison plot in Fig. 8, where the simulation was performed using the measured Moomba pressure as the boundary condition instead of the flow. This type of situation could occur quite readily during normal operation. The program will need to cope with them so that the errors are not carried too far into the future or give rise to false alarms. The practice of using the last good reading if the latest one is faulty is inadequate, especially when the SCADA does not indicate the readings to be faulty, as in this case. We would like to propose a more advanced safety mechanism. Usually the choice of the boundary condition at most sites is between the flow and pressure. Therefore, a mechanism whereby the program detects a faulty reading and switch to the other variable as the new boundary condition would be a powerful tool for keeping the simulation online. The probability of both the flow and pressure readings being faulty is far smaller than the probability of a single instrument error.
W.J. TURNER et al.
0.0
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Fig. 8. Moomba flow with pressure as b.c.
The exact algorithm for the automatic detection of the faulty readings and the switching of the boundary conditions is currently being considered. For this study, the boundary condition was switched manually from the Moomba flow to the Moomba pressure at 147 h. The comparison is certainly much better as is seen in Figs. 9a and b (cf. Figures 7a and b) and the switch does not adversely affect other results.
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(b) Bulla Park pressures with a b.c. switch.
(a) Moomba pressures with a b.c. switch.
Figure 9
4.1.1.
Pipe Roughness
The pipe roughness is the dominant factor contributing to pressure losses within gas pipeline networks. The great distance between valves (greater than 10 km.) and the large curvature (if any) of the pipelines mean that pressure losses associated with valves, bends, etc., in the network are negligible. The precise values of pipe roughness are difficult to acquire experimentally and thus they have been used traditionally as tuning factors. A good on-line simulation program will need to continuously tune them as they have a tendency to increase with time. For this study, a single value (8 pm) was applied to the whole network. There were no factors to justify assigning different roughness to different parts of the network and it was pleasing that excellent sets of agreements could be obtained from a single value of roughness. Figures 10a and b show the effect of changes to the pipe roughness on the pressures at Moomba and the flow at Wilton. Increased roughness causes a lower flow at Wilton and a rising pressure at Moomba, and vice versa. The causes for such behaviour are discussed below. 4.1.2.
Dnving
Functions
Table 2 shows two of the many combinations of driving functions (boundary conditions and device set points) possible in the TPA network. Option 1 represents the configuration used for this study, and option 2 represents an alternative configuration. Moomba is thus a flow-controlled (W) site in option 1, but pressure-controlled (P) in option 2. For option 1, both Bulla Park discharge and Wilton are pressures, thus uniquely determining the flow-rate in the lower section of the network for a given roughness (Figure lob). Unless an
Gas pipeline simulation program
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Figure 10 Table 2. Driving function configurations.
exactly correct value of roughness is taken, the average calculated flow-rate in this section will not equal the average flow-rate imposed on the upper section of the network. The consequence of this is a build up or depletion of gas in the upper section of the network if the roughness is either too high or too low respectively. The effect is seen in Figure 10a at Moomba. If no correction was made eventually the compressor at Bulla Park would either shut down or run at maximum power, respectively and the average flow in the lower section would no longer be determined using the SCADA discharge pressure, but by zero pressure drop across the compressor, or maximum power respectively. Another consequence of option 1 is seen on Fig. 4a. The roughness value had been chosen by minimising the mean difference values. This has meant that a slightly incorrect roughness value was chosen so that the mean difference values which were initially positive were forced to be negative so that overall sum of it were approximately zero for the simulation. A slightly larger, and more correct, roughness would have prevented this drift but the mean difference line would have stayed horizontal at around 0.1%. With option 2, such phenomena cannot occur. Flow on the lower section is set irrespective of the roughness values, and this in turn sets the flow-rate on the upper section. There is no build up or depletion of gas in the simulated network, no changes to the flow, and only the pressures in the network are at a higher or lower level, as seen in Figs. lla and b.
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W.J.
10
TURNER
et
al.
The option 2 is inherently a more stable set-up because steady state solutions can be achieved for a given set of boundary condition values at differing values of pipe roughness. Such set-ups are more desirable for on-line applications since the effects on the simulation of an error in roughness is limited whereas with option 1 it can be very large. 4.2. Flow Comparisons In a network extending over 1500 km, flow measurements were available at only a few sites. This is due mainly to the economics of the operation-flow meters are expensive and they disrupt the flow. Flow measurements were available only at the supply point, all the demand points, and at the compressor site. TPA has noticed a 1% difference between the amount of gas going in and the amount coming out of the network. The observation raises questions about the reliability of the flow meters and we were interested in seeing how the simulated flows values compare with the measured values. Most of the flow measurements were used as boundary conditions, and comparisons between simulated and measured flow values were possible only at Wilton and Bulla Park, the main demand point and the compressor site, respectively. The plots in Fig. 12 show the comparison results in terms of differences and the percentage differences.
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The results of the comparison at Wilton were certainly acceptable as is seen in Figs. 12a and b. The sharp discrepancy at 80 h is due to the constancy of measured values for a period of time. This is the effect of the negative status flags causing the simulation to use the last correct reading until the flag cleared. Figures 12c and d show more serious problems at Bulla Park. For the first 33 h, there was practically no correspondence between the simulated and measured values. After this initial period, some semblance to proportionality was observed, but at about 5 to 15% difference between the simulated and measured values. This was surprising as the suction pressure comparison at the same site had been excellent.
11
Gas pipeline simulation program
The flows and pressures at Moomba (see Figs. 8 and 9a) and the suction and discharge pressures at Bulla Park (see Fig. 7b) remain roughly constant for the whole period to 33 hours. This is reflected in the simulated Bulla Park flows; yet the measured flow displays none of this but goes through a diurnal pattern of peaks and troughs which is repeated throughout the simulation period (repressed somewhat during the weekend). This seems to indicate a settling period, but the lengthiness of the period and the abruptness of its end cast some doubts. Further observation has shown that division of the simulation period at 33 h was misleading. The spike at 33 h in calculated flow simply corresponded to the step increase in the compressor discharge pressure, as can be seen in Fig. 7b. The overall differences are unacceptably large but since the flow meter at Bulla Park does not give out differential pressure values as do other flow meters in the network, the attempt to locate the source of the error is greatly hampered. The flow meter is a part of the Dresser-Clark 990 compressor installed in Bulla Park. We believe that therein lies the probable cause of the error in the Bulla Park flows. The meter is of a different type to others used in the network and there is no other independent method of checking its accuracy. 4.3.
Temperature
Comparisons
Thermal models have long been a neglected aspect of gas pipeline simulation [3]. Attempts are made in this paper to address the deficiencies. The ground temperature tuning mechanism above and the study of compressor temperatures below are some of the examples. Figure 13 shows the simulated and measured temperatures of the gas at the compressor suction. It shows, as was expected, little variation since the pipelines are underground. m.0
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Figure 14 shows four variables versus time. The top curve is the temperature of the gas stream in the compressor discharge header (see Fig. 15). These are simulated values only as the measured values are not available from the SCADA system. The bottom curve is the plot of the ambient temperatures. The middle two are the simulated and measured temperatures of the discharge stream from the cooler. More variations are shown than on the suction side of the compressor. Still the comparison results are good and certainly acceptable-up to 103 h. The comparison after 103 hours is poor. Because of our previous experience in compressor modelling [18], we were confident of the simulated discharge header temperatures. Therefore we suspected the simulation of the forced draught air cooler (see [18] for a more detailed description on how SIROGAS models compressor stations). Figure 16 shows the heat transfer coefficient (kc) of the cooler over the test period. The coefficients were calculated based on the measured values of cooler discharge temperature. The relevant equation is
ws(Ho -
Ho) = k
(TD
- TAIR),
where W, H, T are flow, enthalpy and temperature, respectively. The rough but discernible step change of Lc from 2 x lo6 to 1 x lo6 w/K] at about 100 h suggested that the cooler is a multi-fan set-up and that half the fans were turned off at this point. TPA confirmed this. This fact had not been known to us and we had used a fixed /ZC value of 2 x lo6 for the simulation. It was disclosed that the cooler contains four fans of which multiples of two could be turned on
12
W.J.
0.0
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TURNER et al.
TItE thl Fig. 14. Temperature prdiles at Bulla P&C.
Fig. 15. Compressor station.
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Fig. 17. Compressor discharge texnperatures with cooler correction.
or off based on a simple algorithm. Their implementation of the algorithm was semi-automatic and this was simulated by manually setting the cooler kc to half the original value at 103 h. Substantial improvements were achieved (Figure 17). The temperature profiles along the main pipeline (Figures Ha, b, and c) show a regular pattern over the whole simulation period. The gradual and slight decline along the pipeline is due to the thermal interaction between the cooler ground and the warmer gases. The large peak at around 600 km is of course due to the Bulla Park compressors. The extra large spike in Fig. 18~ is associated with the step increase in the Bulla Park discharge pressure. The profiles would have been very different had the compressor been turned off and/or restarted during the simulation period. The temperatures at Moomba and Wilton were the boundary conditions. Some of the thermocouple readings from sites near Wilton should be regarded with caution. The sharp peaks and troughs are predicted by the program only because of the ground temperature tuning mechanism. This is the weakness of such mechanisms. There is a compressor at the inlet site of Moomba but it has not been simulated for this study since it is not the property of TPA and data were not available. The fluctuating temperatures at Moomba reflect the existence of this compressor. 5. CONCLUSIONS The results of the comparisons were generally satisfactory as is shown by the plots of the means and confidence intervals for differences between measured and calculated values of the major variables. The degree of difficulty in simulation was high because of the relatively large size of the network and the lengthy simulation period. It was compounded by the complexities in simulating compressor stations. The implicit finite difference scheme for the partial differential equations and the equation of state used by SIROGAS are two factors, which we believe, made a successful simulation possible in these situations. Significant discrepancies in the comparison results were observed in four areas. First, the pressure readings at Booberoi were consistently higher than the simulated values. The initially
13
Gas pipeline simulation program
2E..of 4.0
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.
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Figure 18
suspected altitude values used in the simulation were found not to be the cause. The pressure meter at the site is suspected as the source of the problem. Second, the simulated pressures at Moomba and Bulla Park simultaneously diverged from the measured values. This was caused by faulty SCADA readings from a boundary condition site (Moomba flow). The situation was corrected by replacing the Moomba flow by the Moomba pressure as the boundary condition. This we called the “boundary switching concept. Third, the simulated flow values at Bulla Park were significantly lower than the measured values once the simulation settled. The fact that the flow-meter at the site is part of the compressor and different to all other flow-meters in the network gave us cause to doubt the readings. Since, unlike the supply or demand point flows, the compressor flows are not used for accounting, and as the flow comparisons at the main demand point (Wilton) had been excellent, the matter ~8s not pursued. Finally, the simulated and measured temperatures of the compressor discharge stream suddenly diverged during the simulation period. The cause was found to be in the simulation of the cooler at the compressor site. The cooler was not a static device as assumed in the simulation but the number of active fans could be varied according to the ambient temperatures. The problem was largely rectified once this was reflected in the simulation through changes to the cooler heat transfer coefficient. The process of acquiring the correct roughness for the network was the crucial step in having the correct set-up. A single value of roughness was applied to the whole network. There ~88 no need to fine-tune the simulation by having differing roughness values for different sections of the network. The roughness can be modified on-line as a self-tuning factor but care must be taken SO that measurement and other errors are not compensated for instead. An algorithm which will compensate for faulty SCADA readings will need to be developed. The practice of using the value of the last correct reading if the latest one is faulty is workable but obviously not optimal. This would be even less so for the SCADA systems which do not provide status indicators. The concept of switching boundary conditions is a useful one which should be incorporated into such algorithms. Some aspects of the switching concept need to be developed
14
W.J.
TURNER d al.
further. First, there has to be a reliable mechanism to detect faulty readings. Second, there haa to be a gentle transient when switching boundary conditions. Third, it should account for the situations where both readings are faulty. Also the program must transmit reliable information to the operators so that manual actions can be taken instead. We have shown that for normal operation of gas pipeline networks, there is an optimal set of boundary conditions. The effects of drifting roughness values or flow measurement errors can be severe for certain sets of boundary conditions. A test for optimal boundary condition configuration has come out of this study. The test is to see whether steady-state solutions can be achieved for a given set of boundary condition values at differing values of pipe roughness. If so, such configurations should be relied on to simulate pipeline networks where the possibility of drifting roughness is real. Finally, the current situation of inadequate thermal data from the SCADA systems needs to be rectified. Genuine simulation of the thermal model had not been possible with the set of thermal SCADA data provided. It is hoped that future SCADA systems will be able to satisfy this need. REFERENCES 1. L. Bilhnann and R. Isermann, Leak detection methods for pipelines, Aulomatica 23 (3), 381385 (1987). 2. W. Issel and P. Swiger, LASP-A leakage alsystem for pipelines, Pipeline Industry, 26-31 (June 1985). 3. R.E. Murphy, P.D. Dean and E. Gordon, Improved pipeline leak and rupture detection, Pipeline Industry, 25-28 (Oct. 1985). 4. K. Watanabe and D.M. Himmelblau, Detection and location of a leak in a gas transport pipeline by a new acoustic method, AIChE Journal 32 (lo), 1690-1701 (Oct. 1986). 5. P. Higgins, Advances in leak detection systems-Part 1, Pipeline Industry, 29-32 (March 1983). 6. P. Higgins, Advances in leak detection systems-Part 2, Pipeline Industry, 55-58 (April 1983). 7. II. Siebert, Simple method for detecting and locating small leaks in a gas pipeline, Regelungstechnit RT 29 (6), 183-188 (June 1981). 8. M.A. Stoner, T.E. Richwine and F.J. Hunt, Analysis of unsteady flow in gas pipelines-Design to on-line, A.G.A. Operating Section Proceedings, Washington, DC, USA, May 3-5, 1982. 9. W.J. Turner and N.R. Mudford, Leak detection, timing, location and sizing in gas pipelines, Maihl. Compul. Modelling 10 (8), 609-627 (1988). 10. W.J. Turner, Better leak detection in gas pipelines, CornpaL Math. Applic. 15 (l), 69-75 (1988). 11. N.C. Butler, Pipeline leak detection techniques, Process Engineering, 31-35 (July 1982). 12. M.J. Chapman, R.P. Jones and A.J. Pritchard, State observers for monitoring gas pipelines, IEEE Proceedings 134, Pt.D (2) (Mar. 1987). 13. G. Lappus and G. Schmidt, Supervision and control of gas transportation and distribution systems IFAC/IFIP, Conference on Digital Compuler Applicalions to Process Control, Dusseldorf, 14-17 Oct. 1980. 14. W.J. Turner, N.A. Bakker and M. Severs, Simulation of a natural gas pipeline network, Proc. 5th Biennial Conj. of Ihe Simulation Society of Auslralia, University of New England, Annidale, Australia, 1982. 15. W.J. Turner and M.T. Rainbow, NAIAD-A package for modelling flow networks and heat transfer systems, Cont. On Computers and Engineering, Sydney, Australia, Aug. 31-Sept. 2, 1983. 16. A. Osiadacz, Optimal numerical methods for simulating dynamic flow of gas in pipelines, Ink J. Numer. Methods Fluids 3 (2), 125-135 (Mar.-Apr. 1983). 17. K.E. Starling, Fluid Thermodynamics for Light Petroleum Sy&ems, Houston, Gulf, (1973). 18. W.J. Turner and M.J. Simonson, Compressor station transient flow modelIed, Oil and Gas Journal (May 20, 1985).
EVALUATION
Copyright@
OF A GAS PIPELINE W. J. TURNER,
CSlBO
1991
P.
S-J.
KWON
0395-7177/91 $3.00 + 0.00 1991 Perg8mon Press plc
SIMULATION AND P.
Division of Mineral and Process Engineering,
A.
PROGRAM
MAGUIRE*
Lucas Heights Research Laboratories
Private Mail Bag 7, Menai 2234, New South Wales, Australia (Received July 1989 and in revised form
October 1990)
Abstract-As with any predictive scientific tool, the best test of 8 simulation progr8m is to compare its simulated results with values from the real world. A 7-day set of SCADA measurements from a transportation pipeline network extending over 1500 km is used for this purpose. The results of the comparisons with 8 gas pipeline simulation program, SIROGAS, are presented. The objective was to observe and possibly improve its suitability for use as an on-line leak detection tool by testing the capability of the progr8m to simulate 8 large network. The method ~8s to set some of the SCADA data as boundary conditions and compare the remainder with the simulation results. The results 8re quantified through the means and statistical confidence intervals for differences between measured and simulated values of the major variables. The results sometimes indicated abnormalities in the network instrumentation. The performance of the compressor model is discussed and 8 mechanism is suggested for smooth simulations while receiving temporarily erroneous SCADA readings. The effects of boundary condition configurations on simulation 8re also discussed.
1. INTRODUCTION Many differing and ingenious methods have been published in the literature for detection, location, and sizing of leaks in gas pipelines [l-4]. It is generally agreed that transient simulation programs offer one of the more complete solutions to the problem of leak detection [3,5-lo]. Butler [ll] articulated four requirements of an ideal leak detection system; sensitivity, accuracy of leak location, fast response time, and absence of false alarms. The challenge for current simulation/leak detection programs is to satisfactorily meet these requirements in realistic networks in which compressors start and stop from time to time, and such large pressure changes occur that the nonlinear governing equations must be used. there must be a non-trivial leak detection mechanism To meet the above requirements, (e.g., [9,10]), coupled with a suitable simulation program which can closely monitor the operation of a network. Seemingly, the best way to test the suitability of a program is to compare the simulation results to the measured data from an existing pipeline network. Such data are readily available through the Supervisory Control And Data Acquisition (SCADA) systems that are at present integral parts of many pipeline networks. There have been only a few papers on this type of comparisons, seemingly supporting the declaration of Watanabe and Himmelblau [4] that “these methods are quite interesting but are of purely theoretical nature, validated only by computer simulations or by simplified experiments.” One such paper was based on a 80 km section of the British Gas network, used by Chapman, Jones, and Pritchard [12] to test their transient program. They were for a simulation period of 4 h at a sampling interval of 10 min and another at 45 and 3 min respectively. Their study was on a single length of pipe with an off-take valve located halfway along the pipe. It consisted mainly of simulations of a steady state pressure profile, a linearly decreasing pressure versus time profile, and a pressure versus time profile for a short period after an opening of a valve. *The SCADA data were provided by The Pipeline Authority (TPA) of Australia. Discussions with personnel of the TPA, especially Mike Severs, were of considerable aid in the work. Thanks 8re 8lso due to Leeds 8nd Northrup of Australia and Rosemount Instruments for their contribution. Typeset by A&-‘QX I
W.J.
2
TURNER et al.
A larger and more rigourous test was applied by Lappus and Schmidt [13] to their isothermal model of a 75 km long section of a gas transportation network. The simulation was performed for a six-hour set of measurements available from the SCADA system at five-minute intervals. Their main conclusion was similar to the above study in that it was claimed that after a reasonable initial settling-in period, the simulated values converged to the measured values in all cases. Again, the network studied was a simplified set-up in that no devices such as the compressors were present and the length of the pipeline and the duration of the simulations were short. The comparisons presented in this paper are of a more testing nature than those stated above. The simulated pipeline network is larger, has a two unit compressor station and several side streams. The transients are induced by the irregular and cyclic pressure and flow variations as read from the SCADA system. The lengthy interval over which the simulation was performed ensures that relatively thorough tests were made on the program’s ability to simulate many of the situations possible in gas pipeline operations. The ability to simulate the compressor station was also tested in that only the compressor discharge pressure was input to the simulation. 2. THE
PROGRAM
(SIROGAS)
SIROGAS is a FORTRAN 77 program for simulating steady state and transient behaviour of gas flowing in pipeline networks [14,15]. The program considers a network to consist of two entities: pipes and connections. The fluid flow in each pipe is modelled by the partial differential equations for one dimensional gas flow [16]. The three equations are (a) conservation of mass, (b) conservation of momentum, and (c) conservation of energy. All terms are included in the equations; even kinetic and gravitational energy. The pipes are joined by connections representing a wide range of hydraulic components. In SIROGAS, these may be pipe junctions, demand or supply points, compressors, non-return valves, regulators, pipe breaks, tanks, and other components. The detailed compressor model includes recycle, a cooler, and station pressure loss. The linearised implicit finite-difference equations for the pipes and all connection equations except the sonic flow equation are solved by direct solution procedures. SIROGAS includes the capability to compute the variation in gas composition in both space and time, and its effect on the gas flow. Hence the evaluation of the thermodynamic properties of the gas mixture at each node and time step is an important part of SIROGAS. Starling’s generalized equation of state [17] for light petroleum mixtures is used. This general equation contains several coefficients which are functions of gas composition. The relevant values of the coefficients are used to calculate the gas temperature, density, entropy, and other parameters from the pressure and enthalpy values available at every finite difference node. Starling’s published procedures for solving his equations are not used; instead a highly efficient procedure has been developed which converges in one or two iterations in almost all cases. The program is used extensively throughout Australia for design and case study applications and is being installed as a real time model in the new SCADA system for the AGL network in Eastern Australia. 3. THE
SIMULATION
The gas pipeline network studied (Fig. 1) is operated by The Pipeline Authority (TPA) of Australia who are responsible for the transportation of natural gas from Moomba in central Australia to the Wilton interchange near the East coast. All of the natural gas supplied to the Sydney metropolitan area and the industrial centres of Newcastle and Wollongong passes through this interchange. TPA is also responsible for the supply of natural gas to Canberra, the capital city of Australia. The network extends over 1500 km and there is a two-unit compressor station located midway at Bulla Park, 578 km downstream from Moomba. The compressors used are Dresser Clark DC-990 model 553P2. Since this study a second two unit station has been installed at Young 1003 km downstream from Moomba.
Gas pipeline simulation program
3
Fig. 1. TPA network.
Flows are measured at all demand and supply points by DP devices (6 measurements). Pressures and temperatures are measured at these points and at all main line valve sites by diaphragm pressure meters and platinum RTD probes, respectively (31 measurements each). Typical operating pressures (around 6 MPa) and temperatures (around 20 “C) are as shown in Figs. 5a and 18a. Typical flows at the main supply and demand points are shown in Figs. 8 and 12a, respectively. Flows of 50 kg/ s correspond to 250,000 standard M3/h. The SCADA data supplied by TPA covered over 165 hours from Monday the 8th to Sunday the 14th of June 1987 at one minute intervals. The readings were taken from the historical files written routinely by the Leeds and Northrup (Australia) SCADA system. Hence the simulation program received exactly the same data as would a real time model for leak detection. The Leeds and Northrup system transmits all SCADA readings with a numeric flag indicating the status of the reading. This enables the program to take action based on this flag. Throughout this simulation, the action was to take the value of the last correct reading if the status of the current reading is unsatisfactory. Figure 2 shows how the TPA pipeline network is represented as a SIROGAS network of pipes, connections, and finite difference nodes. The number of nodes in a pipe is roughly proportional to the length of the pipe. The compressor station at Bulla Park is represented as two connections, one at the suction and the other at the discharge side. This is a consequence of the way in which SIROGAS simulates gas-powered compressors. Compressors in SIROGAS can operate in several modes; in this simulation discharge pressure control mode was used.
Fig. 2. Schematic representation of a TPA network.
W.J. TURNER et al.
4
SIROGAS allows either pressure or flow controlled boundary conditions. After some experimentation, we took flow-control at all boundary condition points except at the main demand point (Wilton). This was later found to be not an ideal configuration. The reasons will be discussed in Section 4.1.2 where we will show why some boundary condition configurations are to be preferred over others. The main reason for choosing the above configuration was the presence of pressure noise at Moomba, presumed to be from the centrifugal compressor in the Moomba gas plant. The compressor station was simulated with the SIROGAS 2 unit compressor station including the complete manufacturers performance chart for both angular velocity and efficiency. Both suction and discharge pressures and temperatures were available from the SCADA, but only the discharge pressure was used as input to the model; the suction pressure, and the two temperatures being calculated by the model. The fuel usage and the compressor heating were both found to have a significant effect and were calculated by the model. The amount of data from the SCADA system was insufficient to define its dynamic state. Hence the initial conditions were acquired by running a steady-state calculation of the network using some of the SCADA variables as boundary conditions. At the start of the simulation, there were discrepancies between the simulated values and some of the SCADA values not used as boundary conditions. As expected, some settling period was required before the simulated values began to track the SCADA values. 3.1.
Thermal
Model
The main difficulty in testing the thermal model of programs such as SIROGAS is that there are not enough thermal data available from the SCADA systems. A good thermal model needs to take account of the ground temperatures along the pipeline as well as the gas temperatures. The pipe wall temperature readings would also be a bonus. The ambient temperature readings are essential for instances in which the pipes are above ground or when the gas is being aircooled, as in the compressor station site. But much of the above-mentioned data, except the gas temperatures and occasional ground and ambient temperatures, are not available from the normal SCADA systems. Therefore there are insufficient data to drive the thermal model and even less for comparisons. Two major assumptions were made to define the thermal model. First, a mechanism was developed to tune the ground temperature profiles. Within this mechanism, the ground temperatures were assumed to be a complex time-weighted function over 240 minutes of measured gas temperatures. The exact algorithm will not be discussed in this paper but this mechanism ensured that a realistic ground temperature profile was available and although the long-term comparison between the measured and calculated gas temperature could be biased, short-term comparisons were independent and valid. Note that within the network, gas temperature changes occur as a result of changes in altitude and location, as well as compressor station heating. Second, the non-SCADA, hourly readings of the ambient air temperature at the Bulla Park compressor site were available for the whole period. These were used in conjunction with the SCADA data to drive the compressor simulation; in particular the cooling of the discharge gas by forced air draught coolers. 4. RESULTS
AND ANALYSIS
Figures 3a, b, and c represent the mean of the differences between the simulated values of flow, pressure, and temperature from the corresponding measured values, respectively. Absolute values were used so that magnitude of the differences could be seen. The existence of the settling time is confirmed and it can be quantified at about 10 hours as seen from the pressure and flow plots. The plots show that the mean differences do not go beyond 5.0% for flow, 0.5% for pressure, and 0.5 ‘C for temperatures. Figures 4a and b show the means and the 95 and the 99% confidence intervals of the differences in pressures and temperatures. The confidence interval plots are based on the standard deviations of the differences calculated each time step. A comparision of flows is not presented, as there were insufficient measurement points.
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It should be recognized that pressure is the single most important variable to monitor. The pressure readings are more reliable than the flow readings and also more readily available than either the flow or temperature readings. They are also the basis of most leak detection packages predicted (e.g., [9]), along with th e fl ow readings at the supply and demand points. Accurately pressure values are vital for successful simulations. The pressure plots of Fig. 4a show a narrow band-width (at most 0.5%) and mean differences that stay around zero, except for the first 10 h, which as stated above is the initial settling period. The low mean values are of course not entirely accidental, the simulation had been designed and optimized with respect to the mean differences to give minimum overall sum by choosing the roughness values. This is called the roughness tuning. Another noticeable aspect of the plots are the gently sloping nature of the bands towards the negative region. This will be further discussed in Section 4.1.2.
W.J.
6
TURNER
et aI.
In the following sections site names will be frequently mentioned and they are listed in Table 1, together with the distances from Moomba and pipe diameter. To visualize the network, Table 1 should be read in conjunction with Figs. 1 and 2. Table 1. TPA network sites. Distance (km)
Site Booberoi
825
0.847
Bulla Park
578
0.847
Canberra Moomba young Wilton
4.1. Pressure
Diameter (m)
1142
0.266
0
0.847
1033
0.847
1298
0.847
Comparisons
Figures 5a, b, and c show the pressures versus distance along the main pipeline. The plots were taken at three different times to give the instantaneous views of the pressure profile. As the pipeline goes through the hilly inland regions of New South Wales and down the coastal hills to the flat estuarine regions of Wilton, there is a general drop as well as slight fluctuations in pressure. These are understandable in terms of the geography and also of the friction within the pipes. The slight increase in the pressure at the end of the pipeline is due to the drop in altitude near Wilton. The relatively large discrepancies between the measured and calculated pressures in Fig. 5a (at time 0) are due mainly to the effect of simulation starting from steady state as mentioned earlier. In Figs. 5b and c, the largest non-systematic discrepancies are of around 30 kPa-that is about 0.5% of the normal operating pressure.
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The only source of systematic discrepancy is at Booberoi. Initially we suspected the altitude value used in the simulation to be incorrect. Extensive checks revealed that while the altitude could be in error by up to 20 m this is well below the 50 to 60 m drop in altitude required to bring the comparison result into line. Instrumentation error is likely.
Gas pipeline simulation program
7
Moomba, Young, Canberra, and Bulla Park are the network supply point, a junction, a demand point, and the compressor site respectively. Figures 6a, b, 7a and b show the simulated and measured pressures at these sites; these comparisons give a good indication as to how the simulation has performed.
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Figures 6a and b show two excellent sets of results at Young and Canberra. The slight but consistent discrepancy at Young could be tuned out using the pipe roughness as a calibration factor (see Section 4.1.1). The results at Moomba and Bulla Park, shown in Figures 7a and b, were not so easy to interpret. Large discrepancies between the simulated and measured values occurred after 150 h.
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The problem was found to be due to the program getting incorrect SCADA readings for the flow at Moomba which is used as a boundary condition. The error in the flow reading at around 150 h can be seen from the Moomba flow comparison plot in Fig. 8, where the simulation was performed using the measured Moomba pressure as the boundary condition instead of the flow. This type of situation could occur quite readily during normal operation. The program will need to cope with them so that the errors are not carried too far into the future or give rise to false alarms. The practice of using the last good reading if the latest one is faulty is inadequate, especially when the SCADA does not indicate the readings to be faulty, as in this case. We would like to propose a more advanced safety mechanism. Usually the choice of the boundary condition at most sites is between the flow and pressure. Therefore, a mechanism whereby the program detects a faulty reading and switch to the other variable as the new boundary condition would be a powerful tool for keeping the simulation online. The probability of both the flow and pressure readings being faulty is far smaller than the probability of a single instrument error.
W.J. TURNER et al.
0.0
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120.0
160.0
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Fig. 8. Moomba flow with pressure as b.c.
The exact algorithm for the automatic detection of the faulty readings and the switching of the boundary conditions is currently being considered. For this study, the boundary condition was switched manually from the Moomba flow to the Moomba pressure at 147 h. The comparison is certainly much better as is seen in Figs. 9a and b (cf. Figures 7a and b) and the switch does not adversely affect other results.
;jy-yyJy1 * 0.0
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(b) Bulla Park pressures with a b.c. switch.
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Figure 9
4.1.1.
Pipe Roughness
The pipe roughness is the dominant factor contributing to pressure losses within gas pipeline networks. The great distance between valves (greater than 10 km.) and the large curvature (if any) of the pipelines mean that pressure losses associated with valves, bends, etc., in the network are negligible. The precise values of pipe roughness are difficult to acquire experimentally and thus they have been used traditionally as tuning factors. A good on-line simulation program will need to continuously tune them as they have a tendency to increase with time. For this study, a single value (8 pm) was applied to the whole network. There were no factors to justify assigning different roughness to different parts of the network and it was pleasing that excellent sets of agreements could be obtained from a single value of roughness. Figures 10a and b show the effect of changes to the pipe roughness on the pressures at Moomba and the flow at Wilton. Increased roughness causes a lower flow at Wilton and a rising pressure at Moomba, and vice versa. The causes for such behaviour are discussed below. 4.1.2.
Dnving
Functions
Table 2 shows two of the many combinations of driving functions (boundary conditions and device set points) possible in the TPA network. Option 1 represents the configuration used for this study, and option 2 represents an alternative configuration. Moomba is thus a flow-controlled (W) site in option 1, but pressure-controlled (P) in option 2. For option 1, both Bulla Park discharge and Wilton are pressures, thus uniquely determining the flow-rate in the lower section of the network for a given roughness (Figure lob). Unless an
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Figure 10 Table 2. Driving function configurations.
exactly correct value of roughness is taken, the average calculated flow-rate in this section will not equal the average flow-rate imposed on the upper section of the network. The consequence of this is a build up or depletion of gas in the upper section of the network if the roughness is either too high or too low respectively. The effect is seen in Figure 10a at Moomba. If no correction was made eventually the compressor at Bulla Park would either shut down or run at maximum power, respectively and the average flow in the lower section would no longer be determined using the SCADA discharge pressure, but by zero pressure drop across the compressor, or maximum power respectively. Another consequence of option 1 is seen on Fig. 4a. The roughness value had been chosen by minimising the mean difference values. This has meant that a slightly incorrect roughness value was chosen so that the mean difference values which were initially positive were forced to be negative so that overall sum of it were approximately zero for the simulation. A slightly larger, and more correct, roughness would have prevented this drift but the mean difference line would have stayed horizontal at around 0.1%. With option 2, such phenomena cannot occur. Flow on the lower section is set irrespective of the roughness values, and this in turn sets the flow-rate on the upper section. There is no build up or depletion of gas in the simulated network, no changes to the flow, and only the pressures in the network are at a higher or lower level, as seen in Figs. lla and b.
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W.J.
10
TURNER
et
al.
The option 2 is inherently a more stable set-up because steady state solutions can be achieved for a given set of boundary condition values at differing values of pipe roughness. Such set-ups are more desirable for on-line applications since the effects on the simulation of an error in roughness is limited whereas with option 1 it can be very large. 4.2. Flow Comparisons In a network extending over 1500 km, flow measurements were available at only a few sites. This is due mainly to the economics of the operation-flow meters are expensive and they disrupt the flow. Flow measurements were available only at the supply point, all the demand points, and at the compressor site. TPA has noticed a 1% difference between the amount of gas going in and the amount coming out of the network. The observation raises questions about the reliability of the flow meters and we were interested in seeing how the simulated flows values compare with the measured values. Most of the flow measurements were used as boundary conditions, and comparisons between simulated and measured flow values were possible only at Wilton and Bulla Park, the main demand point and the compressor site, respectively. The plots in Fig. 12 show the comparison results in terms of differences and the percentage differences.
y m t
70.0
r:
10.0
a Iii
60.0
ki 5 6
0.0
w g
50.0
0.0
:: 0 -10.0
________ E_ CFLCWTIO 40.0
(a) Flow comparison
so.0
120.0 TlflEChl at Wilton
160.0
0.0
(1299 km).
40.0
120.0 TItlE thl
so.0
(b) Flow comparison
160.0
at Wilton
(1299 lag. s
3 t
c
Y t, E
10.0
0.0
:: 0
si w 80.0 120.0 TlIlE[hl (c) Flow comparison
160.0
-10.0
~-zo.o~.‘.‘.“‘.‘.~“.‘.~ 0.0
at Bulla Park
40.0
80.0 120.0 TIHE thl
(d) Flow comparison
(578 km).
160.0
at Bulla Park
(578 km). Figure 12
The results of the comparison at Wilton were certainly acceptable as is seen in Figs. 12a and b. The sharp discrepancy at 80 h is due to the constancy of measured values for a period of time. This is the effect of the negative status flags causing the simulation to use the last correct reading until the flag cleared. Figures 12c and d show more serious problems at Bulla Park. For the first 33 h, there was practically no correspondence between the simulated and measured values. After this initial period, some semblance to proportionality was observed, but at about 5 to 15% difference between the simulated and measured values. This was surprising as the suction pressure comparison at the same site had been excellent.
11
Gas pipeline simulation program
The flows and pressures at Moomba (see Figs. 8 and 9a) and the suction and discharge pressures at Bulla Park (see Fig. 7b) remain roughly constant for the whole period to 33 hours. This is reflected in the simulated Bulla Park flows; yet the measured flow displays none of this but goes through a diurnal pattern of peaks and troughs which is repeated throughout the simulation period (repressed somewhat during the weekend). This seems to indicate a settling period, but the lengthiness of the period and the abruptness of its end cast some doubts. Further observation has shown that division of the simulation period at 33 h was misleading. The spike at 33 h in calculated flow simply corresponded to the step increase in the compressor discharge pressure, as can be seen in Fig. 7b. The overall differences are unacceptably large but since the flow meter at Bulla Park does not give out differential pressure values as do other flow meters in the network, the attempt to locate the source of the error is greatly hampered. The flow meter is a part of the Dresser-Clark 990 compressor installed in Bulla Park. We believe that therein lies the probable cause of the error in the Bulla Park flows. The meter is of a different type to others used in the network and there is no other independent method of checking its accuracy. 4.3.
Temperature
Comparisons
Thermal models have long been a neglected aspect of gas pipeline simulation [3]. Attempts are made in this paper to address the deficiencies. The ground temperature tuning mechanism above and the study of compressor temperatures below are some of the examples. Figure 13 shows the simulated and measured temperatures of the gas at the compressor suction. It shows, as was expected, little variation since the pipelines are underground. m.0
.
I.
(.
I.,
1,.
,
290.0b.‘.““‘.‘.“‘.‘.I 0.0
40.0
120.0
80.0
TIflE
.
,
.
1.
160.0
[h)
Fig. 13. Suction temperature comparison at Bulla Park.
Figure 14 shows four variables versus time. The top curve is the temperature of the gas stream in the compressor discharge header (see Fig. 15). These are simulated values only as the measured values are not available from the SCADA system. The bottom curve is the plot of the ambient temperatures. The middle two are the simulated and measured temperatures of the discharge stream from the cooler. More variations are shown than on the suction side of the compressor. Still the comparison results are good and certainly acceptable-up to 103 h. The comparison after 103 hours is poor. Because of our previous experience in compressor modelling [18], we were confident of the simulated discharge header temperatures. Therefore we suspected the simulation of the forced draught air cooler (see [18] for a more detailed description on how SIROGAS models compressor stations). Figure 16 shows the heat transfer coefficient (kc) of the cooler over the test period. The coefficients were calculated based on the measured values of cooler discharge temperature. The relevant equation is
ws(Ho -
Ho) = k
(TD
- TAIR),
where W, H, T are flow, enthalpy and temperature, respectively. The rough but discernible step change of Lc from 2 x lo6 to 1 x lo6 w/K] at about 100 h suggested that the cooler is a multi-fan set-up and that half the fans were turned off at this point. TPA confirmed this. This fact had not been known to us and we had used a fixed /ZC value of 2 x lo6 for the simulation. It was disclosed that the cooler contains four fans of which multiples of two could be turned on
12
W.J.
0.0
40.0
160.0
120.0
60.0
TURNER et al.
TItE thl Fig. 14. Temperature prdiles at Bulla P&C.
Fig. 15. Compressor station.
0.8 ' 2 5 :
06 * 0.4 0.2
0.0
40.0
60.0
120.0
160.0
290.0 0.0
TIE Chl Fig. 16. Cooler heat transfer coefkient.
. ’ . ’ 40.0
. ’
’
. ’
’
’
80.0 120.0 TIflE(h1
’
’
. ’ ’ 160.0
Fig. 17. Compressor discharge texnperatures with cooler correction.
or off based on a simple algorithm. Their implementation of the algorithm was semi-automatic and this was simulated by manually setting the cooler kc to half the original value at 103 h. Substantial improvements were achieved (Figure 17). The temperature profiles along the main pipeline (Figures Ha, b, and c) show a regular pattern over the whole simulation period. The gradual and slight decline along the pipeline is due to the thermal interaction between the cooler ground and the warmer gases. The large peak at around 600 km is of course due to the Bulla Park compressors. The extra large spike in Fig. 18~ is associated with the step increase in the Bulla Park discharge pressure. The profiles would have been very different had the compressor been turned off and/or restarted during the simulation period. The temperatures at Moomba and Wilton were the boundary conditions. Some of the thermocouple readings from sites near Wilton should be regarded with caution. The sharp peaks and troughs are predicted by the program only because of the ground temperature tuning mechanism. This is the weakness of such mechanisms. There is a compressor at the inlet site of Moomba but it has not been simulated for this study since it is not the property of TPA and data were not available. The fluctuating temperatures at Moomba reflect the existence of this compressor. 5. CONCLUSIONS The results of the comparisons were generally satisfactory as is shown by the plots of the means and confidence intervals for differences between measured and calculated values of the major variables. The degree of difficulty in simulation was high because of the relatively large size of the network and the lengthy simulation period. It was compounded by the complexities in simulating compressor stations. The implicit finite difference scheme for the partial differential equations and the equation of state used by SIROGAS are two factors, which we believe, made a successful simulation possible in these situations. Significant discrepancies in the comparison results were observed in four areas. First, the pressure readings at Booberoi were consistently higher than the simulated values. The initially
13
Gas pipeline simulation program
2E..of 4.0
8.0 DISTRNCE tx1OOkm)
12.0
L
0.0
’ 4.0
.
’
.
’
’
8.0
.
’
.
4
12.0
DISTRNCE ~r100kn~
(a) Temperature vs Distance comparison (0 min.)
275.0
’
0.0
(b) Temperature vs Distance comparison (1000 min.)
4.0
8.0 DISTANCE [x100knl
12.0
(c) Temperature vs Distance comparison (2000 min.)
Figure 18
suspected altitude values used in the simulation were found not to be the cause. The pressure meter at the site is suspected as the source of the problem. Second, the simulated pressures at Moomba and Bulla Park simultaneously diverged from the measured values. This was caused by faulty SCADA readings from a boundary condition site (Moomba flow). The situation was corrected by replacing the Moomba flow by the Moomba pressure as the boundary condition. This we called the “boundary switching concept. Third, the simulated flow values at Bulla Park were significantly lower than the measured values once the simulation settled. The fact that the flow-meter at the site is part of the compressor and different to all other flow-meters in the network gave us cause to doubt the readings. Since, unlike the supply or demand point flows, the compressor flows are not used for accounting, and as the flow comparisons at the main demand point (Wilton) had been excellent, the matter ~8s not pursued. Finally, the simulated and measured temperatures of the compressor discharge stream suddenly diverged during the simulation period. The cause was found to be in the simulation of the cooler at the compressor site. The cooler was not a static device as assumed in the simulation but the number of active fans could be varied according to the ambient temperatures. The problem was largely rectified once this was reflected in the simulation through changes to the cooler heat transfer coefficient. The process of acquiring the correct roughness for the network was the crucial step in having the correct set-up. A single value of roughness was applied to the whole network. There ~88 no need to fine-tune the simulation by having differing roughness values for different sections of the network. The roughness can be modified on-line as a self-tuning factor but care must be taken SO that measurement and other errors are not compensated for instead. An algorithm which will compensate for faulty SCADA readings will need to be developed. The practice of using the value of the last correct reading if the latest one is faulty is workable but obviously not optimal. This would be even less so for the SCADA systems which do not provide status indicators. The concept of switching boundary conditions is a useful one which should be incorporated into such algorithms. Some aspects of the switching concept need to be developed
14
W.J.
TURNER d al.
further. First, there has to be a reliable mechanism to detect faulty readings. Second, there haa to be a gentle transient when switching boundary conditions. Third, it should account for the situations where both readings are faulty. Also the program must transmit reliable information to the operators so that manual actions can be taken instead. We have shown that for normal operation of gas pipeline networks, there is an optimal set of boundary conditions. The effects of drifting roughness values or flow measurement errors can be severe for certain sets of boundary conditions. A test for optimal boundary condition configuration has come out of this study. The test is to see whether steady-state solutions can be achieved for a given set of boundary condition values at differing values of pipe roughness. If so, such configurations should be relied on to simulate pipeline networks where the possibility of drifting roughness is real. Finally, the current situation of inadequate thermal data from the SCADA systems needs to be rectified. Genuine simulation of the thermal model had not been possible with the set of thermal SCADA data provided. It is hoped that future SCADA systems will be able to satisfy this need. REFERENCES 1. L. Bilhnann and R. Isermann, Leak detection methods for pipelines, Aulomatica 23 (3), 381385 (1987). 2. W. Issel and P. Swiger, LASP-A leakage alsystem for pipelines, Pipeline Industry, 26-31 (June 1985). 3. R.E. Murphy, P.D. Dean and E. Gordon, Improved pipeline leak and rupture detection, Pipeline Industry, 25-28 (Oct. 1985). 4. K. Watanabe and D.M. Himmelblau, Detection and location of a leak in a gas transport pipeline by a new acoustic method, AIChE Journal 32 (lo), 1690-1701 (Oct. 1986). 5. P. Higgins, Advances in leak detection systems-Part 1, Pipeline Industry, 29-32 (March 1983). 6. P. Higgins, Advances in leak detection systems-Part 2, Pipeline Industry, 55-58 (April 1983). 7. II. Siebert, Simple method for detecting and locating small leaks in a gas pipeline, Regelungstechnit RT 29 (6), 183-188 (June 1981). 8. M.A. Stoner, T.E. Richwine and F.J. Hunt, Analysis of unsteady flow in gas pipelines-Design to on-line, A.G.A. Operating Section Proceedings, Washington, DC, USA, May 3-5, 1982. 9. W.J. Turner and N.R. Mudford, Leak detection, timing, location and sizing in gas pipelines, Maihl. Compul. Modelling 10 (8), 609-627 (1988). 10. W.J. Turner, Better leak detection in gas pipelines, CornpaL Math. Applic. 15 (l), 69-75 (1988). 11. N.C. Butler, Pipeline leak detection techniques, Process Engineering, 31-35 (July 1982). 12. M.J. Chapman, R.P. Jones and A.J. Pritchard, State observers for monitoring gas pipelines, IEEE Proceedings 134, Pt.D (2) (Mar. 1987). 13. G. Lappus and G. Schmidt, Supervision and control of gas transportation and distribution systems IFAC/IFIP, Conference on Digital Compuler Applicalions to Process Control, Dusseldorf, 14-17 Oct. 1980. 14. W.J. Turner, N.A. Bakker and M. Severs, Simulation of a natural gas pipeline network, Proc. 5th Biennial Conj. of Ihe Simulation Society of Auslralia, University of New England, Annidale, Australia, 1982. 15. W.J. Turner and M.T. Rainbow, NAIAD-A package for modelling flow networks and heat transfer systems, Cont. On Computers and Engineering, Sydney, Australia, Aug. 31-Sept. 2, 1983. 16. A. Osiadacz, Optimal numerical methods for simulating dynamic flow of gas in pipelines, Ink J. Numer. Methods Fluids 3 (2), 125-135 (Mar.-Apr. 1983). 17. K.E. Starling, Fluid Thermodynamics for Light Petroleum Sy&ems, Houston, Gulf, (1973). 18. W.J. Turner and M.J. Simonson, Compressor station transient flow modelIed, Oil and Gas Journal (May 20, 1985).