TUTORIAL ON SOFTWARE BASED LEAK DETECTION TECHNIQUES. Dr. R. S. Whaley, R. E. Nicholas, J. D. Van Reet Scientific Softwaredntercomp

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1 TUTORIAL ON SOFTWARE BASED LEAK DETECTION TECHNIQUES Dr. R. S. Whaley, R. E. Nicholas, J. D. Van Reet Scientific Softwaredntercomp

2 R S. Whaley, R. E. Nicholas, and J. D. Van Reet Scientific Software - Intercomp ABSTRACT The authors present a non-commercial overview of the various computer based methods for performing leak detection on pipelines in real-time. The paper includes descriptions of over/short analysis, single point pressure analysis, deviations analysis, and model compensated volume balance techniques. i%e strengths and weaknesses of each of the methods is discussed along with the instrumentation and installation requirements necessary for success$ul implementation. The effectiveness of the metho& as a function of pipeline operating conditions or philosophies is also explored. I INTRODUCTION Detection of leaks on pipelines using computer based methods is a subject not without controversy over the past twenty years. Several techniques have been implemented on many different pipelines with various levels of success. The success, or lack of it, on any given pipeline is a complicated function of many variables ranging from the expectations of the pipeline company and the commitment of the vendor to quality to the technical solution employed. The techniques employed to detect leaks using software solutions are also quite varied. Often, it is difficult for a pipeline company to discern what is the best solution for their particular pipeline and philosophy of operation. This paper will attempt an explanation of the most popular software solutions used to detect pipeline leaks. Instrumentation requirements will be described. It will also include a discussion of the strengths and weaknesses of each method and the types of pipelines on which each performs the best. Some discussion of the cost of installation of each method is also presented. 2 VOLUME BALANCE One of the earliest computer methods developed to detect the presence of a leak on a pipeline was the Volume Balance Method. The method as it is used today is an extension of the old Over / Short analysis and is based on the principle of conservation of mass. The Volume Balance method is a non-model based leak detection solution. 2.1 Theory of Operation The principle behind the volume balance approach is the conservation of mass. That is, that the amount of fluid that goes into the pipe over any time interval minus the amount that goes Pipcline Simulation Intercst Group, October

3 out of the pipe must be equal to the change in fluid inside the pipe over the same time interval. Expressed mathematically, the relationship is: VB =C J:, Qindt-2 11, Qoutdt - AM in (1) where: Qin Q out A4 are the supply flows into the pipeline segment. are the delivery flows out of the pipeline segment. is the mass of fluid contained in the pipeline segment. By the principle of conservation of mass, the volume balance should be zero in a non-leaking pipe. If a leak should occur in the pipeline segment, the volume balance will become positive by the amount of fluid which has escaped the pipe. 2.2 Calculation of Flow Balance The Q, and Q,,, parameters in equation are usually generated from the flow measurements at all inputs and outputs to the pipeline. The integrals are replaced by summations of the flows multiplied by the SCADA scan interval. I t Qdt =c Qi(ti-tgl to i where the index i is over every SCADA scan interval between to and t. Many pipelines have volume accumulator measurements available in addition to or instead of flow measurements. With volume accumulators, the first two integrals in equation (1) can be replaced with the difference between accumulator readings at times t and to 2.3 Compensating for Line Pack Changes Often the method called Volume Balance refers to one in which only the flows into and out of the pipeline are considered. This would be mathematically represented by only the first two terms in Equation (1). Volume Balance without any correction for changing line pack is most often done on crude oil pipelines or other liquid pipelines that are not batched. The volume balance approach contains the implicit assumption of steady state. That is, that whatever mass is injected into the pipeline over a given time interval is equal to the mass that is delivered. Since the time interval is arbitrary, the pipeline must be assumed to be in steady state all of the time. The smallest leak size which can be detected over any time interval Pipeline Simulation Interest Group, October

4 using this simplified approach is the uncertainty in the line pack. In other words, only leaks which are larger than the additional amount of mass which can be injected into the pipeline over any time interval can be detected. This assumption does not work very well for gas pipelines where the line pack can change by very large amounts over a short amount of time. It does not even work very well for liquid pipelines that transport more than one type of fluid since the propagation of an interface between two liquids with different densities is an unsteady process since the average density of the inventory of the pipeline changes continuously as the interface traverses the pipeline. Consequently the volume balance approach is greatly improved by the addition of the third term in Equation (1). The inventory of the pipeline is usually calculated by assuming steady state pressure and temperature profiles from one end of the pipeline segment to the other and calculating an average density for the segment. The density is then multiplied by the pipeline volume to arrive at the inventory. Using this method, the third term in Equation (1) is simply the difference between the calculated inventories at times t and to Including the change in line pack in the volume balance equation greatly reduces the amount of error in the volume balance associated with the packing or drafting of the line. The volume balance uncertainty is reduced to the error in the flow meters plus the error in the calculated line pack (which is still larger than the error in the flow meters most of the time). In some installations, the calculation of the line pack is extended to include factors such as the expansion of the pipe wall and the effects of temperature changes on the line pack in an effort to reduce the calculated line pack error. 2.4 Use of Varying Time Intervals The sensitivity of the Volume Balance method to detecting leaks becomes greater as the time interval t, to t becomes larger. Primarily, this is because the errors in the line pack are diluted over the longer time period, making the system sensitive to smaller leakage rates. Noise in the instrumentation is a major source of error in the volume balance calculation. Noise is any signal, whether it is electronic noise or true hydraulic signal, which is of a frequency greater than twice the SCADA scan rate. Noise in the flow measurements translates into errors in the flow balance portion of the volume balance equation. By integrating over longer time intervals, the number of discrete flow measurements used in the calculation is increased, thereby averaging out the effect of the noise. Since the calculation of line pack in Equation (1) is a difference of two values at the beginning and end of the time period, the error associated with the calculation is not reduced by extending the time interval over which the calculation is done. Sources of error for the pack term are noise and other error in the pressure and temperature measurements, uncertainty in the batch interface location for liquid lines, and uncertainty in the composition Pipeline Simulation Interest Group, October

5 and properties of the fluid. Even though the error in the change in line pack term is not itself reduced by the longer time period, the sensitivity of the system in terms of leakage rates is improved by diluting the linepack error. The length of the leak detection period that will work effectively is dependent on the pipeline, SCADA scan rate, and instrumentation. As a general rule, leak detection periods below thirty minutes are not effective when using the Volume Balance method since the threshold above which the volume balance is indicative of a leak must be set too high to be acceptable. Reducing the level of the volume balance threshold will produce false alarms whenever a significant transient occurs on the pipeline. In longer time periods, the effects of transients which are short in duration are minimized. Periods of 30 minutes to an hour are not uncommon to detect large leaks and line breaks with the most sensitive leak detection requiring periods as long as 24 hours. 2.5 Strengths & Weaknesses The weakness of the Volume Balance Method is its susceptibility to false alarms generated by the fact that it is based on steady state assumptions while being applied to pipelines that are rarely, if ever, in steady state. In order to keep the rate of false alarms at an acceptable level, time periods of from one hour to one day are required. The response time to detect the leaks is the same as the leak detection period. In many cases, response times of one hour to one day are unacceptably long. Leak location cannot be done using the Volume Balance method. By including in-line flow meters, the pipeline can be segmented. This will improve the sensitivity of the leak detection and allow the leak to be located to within the segment. The Volume Balance method can achieve sensitivities that approach the overall accuracy of the flow meters. Sensitivities of this level are usually only attained with leak detection periods of twelve to twenty four hours. 3 SINGLE POINT PRESSURE ANALYSIS Another leak detection technique that has been widely employed is the use of pressure measurements to detect the depressurization that accompanies a leak. The most common implementations of this are detecting pressures that become abnormally low, and detecting rate of pressure change that become too low (pressures falling too rapidly). These systems may be implemented as software or hardware solutions, and may be remote from or local to the transducers. More recently, attempts have been made to use statistical techniques to determine when a measured pressure is declining in a significant manner as a leak detection technique. We refer to this method as single point pressure analysis. Pipeline Simulation Interest Group, October

6 3.1 Theory of Operation The principle behind the operation of single point pressure analysis is that the pressure in a pipeline will decline as a result of a leak. Further, certain statistical properties can be computed to determine if a pressure is declining in a significant manner. Single point pressure analysis requires that all events other than leaks that may cause a pressure to decline, such as operational changes to the pipeline, must be identified so that the leak detection can be inhibited until such time as the pipeline returns to a steady operation. The technique operates as follows. A buffer of the most recent measured values of pressure is kept for analysis. The data is divided into two periods and the mean and variance of the two samples are computed. P old (2) (3) 2 Oold = noid- 1 %ld =( ial Pi-Fold 2 (4) Pi-F- 2 (5) where: Is Pi cf2 np nofd is the average of the data is the measured pressure at time i is the variance of the data is the total number of data points is the number of data points in the older part of the data the subscripts new and old refer to the older and newer partitions of the data. A leak is suspected when the mean of the newer partition of the data is significantly lower than the mean of the older part of the data. Statistically, we wish to test the hypothesis that F old = Pn,, against the alternative PO, > Fn,. To do this we compute Pipeline Simulation Interest Group, October

7 (6) where CL is the sample mean of the data is the sample variance of the data n is the number of data points in each sample. The subscripts 2 and 3 in equation 6 correspond to old and new in equations 2 through 5. The result of equation 6 is an observed value of a random variable which has a Student s t-distribution with nl degrees of freedom. Using the value computed in 6 and the number of degrees of freedom the significance level of the test can be found from a table of Student s t distribution. This is taken as the probability of a leak being present. 3.2 Implementation The implementation of a single point pressure is a fairly straightforward matter given the theory of operation from the previous section. As the pressure measurements are obtained they are placed in a circular buffer where the most recent np values are kept. Each pressure measurement used in the procedure is treated individually, and as many measurements as desired or available may be analyzed. For each measurement the data is partitioned into new and old sets. Several partitions may be made of the same data. The advantage in doing so is to try to maximize both detection speed and sensitivity. The sensitivity is maximized by using a large sample for both the old and new data partitions, which corresponds to dividing the data set in half. This requires a leak to be present for a potentially long time before it becomes apparent in the mean of the new data, however. By using shorter time periods for the new data partition, which corresponds to putting more data in the old partition, the leak affects the mean of the new data more rapidly. This reduces the sample size of the new data partition, however, and reduces the sensitivity of the test. For each partition of the data the techniques described in section 3.1 are used to determine the probability that the pressure is declining. When this probability exceeds a threshold a leak alarm is issued, if no operational event has occurred which can explain the pressure decline. A critical part of a single point pressure analysis system is the ability to inhibit the system during known transients. Since the leak signature being monitored is a pressure decline, any other operation that might cause a decline in one of the monitored pressures must be identified to avoid false leak alarms. Once such an event is noted, the leak detection system is inhibited until the pipeline returns to a steady operation. Schemes to selectively inhibit monitoring of certain pressure measurements in response to certain events while continuing Pipeline Simulation Interest Group, October

8 to monitor pressures that are not affected are possible. An overview of the operation of a single point pressure analysis system is shown below. The performance of a single point pressure analysis system may be adjusted by varying the number of retained measurements kept in the buffer, the number and size of partitions to make of the data, the method and duration of inhibiting the system after normal operating transients, and the alarm limit for the leak probability. Single Point Pressure Analysis Pressure Measurements from!xada c Partition Data 4 Compute Leak Probabilities 3.3 Strengths and Weaknesses The primary strength of a single point pressure analysis method is its simplicity. This translates into minimal instrumentation and computer resource requirements and therefore lower overall installation costs. The weaknesses of single point pressure analysis center on its use of pressure decline as a leak signature. Because pressure decline is not unique to a leak event, single point pressure analysis can yield false alarms even when the leak signature is correctly identified. This is in contrast to conservation of mass based systems, that fail only when unable to identify the leak signal in the data. This weakness leads to a high probability of false alarms due to unforeseen Pipeline Simulation Interest Group, October

9 operating transients and the need to inhibit the leak detection system during known operating transients. Single point pressure analysis system do not provide location data beyond identifying the transducers involved, and do not provide any estimate of leak size or spill volume. Single point pressure analysis would seem to be best applied to small pipelines transporting incompressible fluid that operate in a nearly steady state mode. Larger systems, lines transporting compressible fluids, and lines with frequent transients would likely experience a high number of false alarms and/or have the leak detection system inhibited a large portion of the time. If a system has flow measurements available, a system based on conservation of mass would provide valuable backup to a pressure analysis system. 3.4 costs Specific cost information for the installation of commercial single point pressure analysis leak detection is not readily available to the authors. License fees for the software are similar to those charged for model based systems being in the range of seventy five to one hundred thousand dollars. Costs for installing the software will vary according to the pipeline application. Short, single leg pipelines require less installation effort while longer pipelines with intermediate pump or compressor stations require more. One of the primary advantages to the method is that, in principle, it requires only a single pressure measurement in each pipeline segment. However, the most successful installations will utilize measurements other than the lone pressure measurements to warn the system of events which will cause transients that have the same characteristics of a leak or provide other information which is important for false alarm avoidance. The inclusion of these additional sensors will increase the costs of installation somewhat. The method is not limited by the accuracy of the flow meters like model based methods are which can result in a cost savings by not requiring the more sensitive meters. 4 TRANSIENT MODEL BASED METHODS The Volume Balance Method relies on the steady state assumption and must use averaging of leak detection data to overcome the effects of transients on the leak detection calculations. The single point analysis depends upon detecting the transients in the pressure measurements and determining whether the transients are caused by a leak on the pipeline. The challenge for the method is distinguishing between leak generated transients and other normally occurring transients on a pipeline. The model based methods attempt to improve sensitivity and reduce Pipeline Simulation Interest Group, October

10 response times by simulating the transients on a pipeline in real time to separate the effects of a leak from all other phenomena. The simulation model is a numerical integration of the equations of motion (momentum and continuity equations) of the fluid in the pipeline along with the energy equation to describe the flow of heat into and out of the fluid. Most real time models used in success&l leak detection systems use an implicit matrix based solution of either the first two or all three equations together. Momentum Equation Continuitv Eauation (v~+vv,)+~~+~+g-&=o fvlvl G!z (pwx + (P4, = 0 Enerw Eauation Tt = -pvc Tx-2-@(vx+.~)+pv+1~+~KvT pc, + ~ppip?cpip Accurately solving these equations for a given pipeline requires a complete description of the pipeline and instrumentation. Parameters such as pipe segment lengths, diameters, and wall thickness are readily available. However, a number of parameters which must be input to the models are not generally well known such as the inside pipe roughness, and the current drift or calibration of the instruments. For a model to accurately calculate the hydraulics and thermodynamics of a pipeline in real time, the software must use tuning or adaptive modeling. Adaptive modeling is the adjustment of certain modeling parameters based on the comparison of simulation results and measured values which were not used in the calculations. The adaptive modeling process can be represented by the following flow diagram. Pipeline Simulation Interest Group, October

11 ADAPTIVE MODELING AUTOMATIC TUNED PARAMETERS L FLOW RATES MODEL 4 PACK RATES The approach generally used to simulate the pipeline flow and thermodynamics in real time and ultimately detect leaks is to 1) acquire pipeline data, 2) use some of the measured values as boundary conditions for the integration of the momentum, continuity, and energy equations, 3) compare the calculated values against measurements not used as boundaries to the simulation, 4) perform leak calculations, and 5) if no leak is present, use the discrepancies between measured and calculated values to adjust tuning parameters. The procedure might be similar to that in the figure below. MODEL BASED LEAK DETECTION FLOW DIAGRAM I I Measuremnts Pipeline Simulation Interest Group, October

12 Simulation model based leak detection is usually accomplished through one of two techniques. These are the Deviations Analysis and the Model Compensated Volume Balance methods. 4.1 Deviations Analysis Deviations analysis is one of the first model based techniques developed for detecting leaks in pipelines. It is a natural outgrowth of the explicit integration techniques which used pressure and flow boundaries to model the pipeline Theory of Operation The Deviations Analysis is based on the principle that by simulating the hydraulics in real time using some of the measurements at the ends of a pipeline segment, a leak can be detected when the computed values (pressure and flow) at the ends of the pipeline deviate from the measurements at the ends of the pipeline not used as boundary conditions. Leak detection systems which are not model based have attempted to work on the premise that a leak will cause a change in a pressure or flow value at the end of the pipeline segment, and that by monitoring the changes in the measurements, the leak can be detected. Unfortunately, the effectiveness of the procedure is diminished dramatically during pipeline transients. The purpose of the simulation model is to provide the capability of detecting the changes in pressure and flow values caused by a leak even in the presence of pipeline transients. By using measurement values taken at frequent intervals as boundaries, the simulation model can track the transient hydraulics of the pipeline. Since changes in endpoint values caused by phenomena other than leaks are accounted for in the model, deviations between the modeled and measured values at the ends of a pipeline segment should be indicative of a leak whether the pipeline is in a transient condition or not. The figure below demonstrates how the deviations analysis works. The upper (light) curve represents what the model produces as the pressure profile in the line. The upstream pressure is fixed because it is being used as a boundary condition to the model. Likewise, the downstream measured flow, F,, is input to the model as a boundary. The leak causes the actual flow (F,) in the upstream end of the pipeline to increase which causes a larger pressure drop in the upstream segment. Downstream of the leak, the flow, F,, is the same as the flow used by the model ( this has to be the case since the model is using the downstream measured flow as a boundary) producing a pressure drop which is the same as that predicted by the model. Pipeline Simulation Interest Group, October

13 DEVIATIONS ANALYSIS DISTANCE The result of the leak is a deviation between the downstream modeled pressure and the measured pressure (P,,,- PO) as well as a deviation between the upstream modeled flow and the measured flow (F,,,- FJ. The magnitude of the pressure deviation is a function of the size of the leak and its location along the pipeline. The magnitude of the flow deviation is the size of the leak. Normal fluctuations in the modeling of the pipeline and in the measurements themselves will cause small deviations between modeled and measured values. Therefore, deviation threshold values are established for each computed endpoint pressure or flow. Below these thresholds, the deviations are accepted as due to noise in the measurements, uncertainties in fluid properties, or to measurement errors or fluctuations of too high a frequency to be modeled. Once a deviation has exceeded its threshold, the condition can be interpreted as a leak. A leak should trigger more than one deviation alarm. However, small leaks located remote from a calculated pressure point may trigger one deviation alarm without triggering any others. In the same manner, a measurement error or fluctuation may cause a deviation to exceed a single threshold when no leak is present. To avoid undue false alarms, the Deviations Analysis is normally implemented with voting schemes in which more than one alarm must exist before a leak condition is enunciated to the operator. The location of the leak can be determined in a number of ways. In the simplest analysis, the location can be determined through a geometric calculation. The flow deviation is used to determine the size of the leak and based on that size along with the pressure deviation, the location of the leak may be deduced. Some systems use a method whereby multiple simulations are run with leaks in different locations. The resulting deviations between modeled and measured quantities are compared with the real-time deviations in an effort to match patterns to find the most probable location of the leak. This latter method is used Pipeline Simulation Interest Group, October

14 Tutorial on Software Based Leak Detection Techniques most often in networks of more complexity figure above. than the single pipeline segment shown in the Strengths & Weaknesses Whereas methods based on the steady state assumption like the Volume Balance approach must average over extended time periods to smooth out the effects of transients and the Single Point Analysis must be disabled in the presence of pipeline transients, the Deviations Analysis, through the use of the simulation model, is able to detect leaks when even large transients are present on the pipeline. The procedure is capable of detecting large leaks in a matter of minutes, with smaller leaks taking around thirty minutes. Through the use of the real time model, leak detection systems employing the Deviations Analysis usually provide other useful functions such as batch and scraper tracking on liquid systems and composition tracking for gasses. Model based methods require more instrumentation than other systems. Like the Volume Balance Method, the Deviations Analysis requires pressure and flow measurements at all inlets and outlets of the pipeline. Temperature measurements are also required on at least the injections on the pipeline. For a complete description of the instrumentation requirements of model based systems, see Section 4.4 below. The Deviations Analysis is dependent on the tuning of the model. Therefore is tends to be susceptible to measurement errors and uncertainties in some of the fluid properties. 4.2 Model Compensated Volume Balance The Model Compensated Volume Balance is an extension of the traditional Volume Balance approach. Like the Deviations Analysis, the method uses a simulation model to compensate for the transients in the pipeline, thus enabling it to produce fast leak detection even in the presence of transients in the pipeline Theory of Operation The Model Compensated Volume Balance approached is based on the comparison in real time between measurement generated flow balances and model generated packing rates. If the flow balance is defined as FB =c F$-x x X Fy: where F and F are the measured flows in and out of the pipeline segment respectively, and the packing rate is the rate of change of inventory in the pipeline Pipeline Simulation Interest Group, October

15 Tutorial on Software Based Leak Detection Techniques PK= FULP2,T1) computed by the model from measured pressures and temperatures at the endpoints of the pipeline segment, then the Volume Balance is defined as: VB=FB-PK Under normal (non-leak) conditions the volume balance should be equal to zero. When a leak occurs, the packing rate will quickly drop and the flow balance will rise in response to the leak. This causes a positive valued volume balance which is indicative of the leak. The figure below demonstrates the behavior of the measurement generated flow balance and the model generated packing rate at the onset of a leak. I ACTUAL LEAK TRANSIENT p:f;* F<Fo MODELED LEAK TRANSIENT 0 The upper half of the figure represents what actually happens as a leak somewhere in the middle of the pipeline develops. The pressures surrounding the leak drop as a result of the loss of fluid. The resulting pressure transient propagates from the leak outward to both ends of the pipeline segment resulting in increased flow at the upstream end and reduced flow at the downstream end. As the new pressure profile is established, the flow balance (flow in minus flow out) becomes more and more positive until a steady condition is established where the flow balance becomes the size of the leak. The lower half of the figure demonstrates the way the leak transient is represented by the model. The model sees only that the pressures at the endpoints of the pipeline are dropping. The resulting transient then propagates in from the endpoints. This causes the Pipeline Simulation Interest Group, October

16 model to predict an increased flow at the downstream end and a decreased flow at the upstream. This condition is consistent with a pipeline which is unpacking (negative PK). As the model tracks the hydraulics of the pipeline, normal transients which cause either positive or negative flow balances will also cause the model to predict the same packing rate. It is only in the presence of a leak that the measured flow balance and the model generated packing rate will diverge. Normal fluctuations in the measurements will cause small deviations between flow balance and packing rate. The Model Compensated Volume Balance method, like other methods employs a threshold under which no leak is declared. When the computed volume balance exceeds the threshold a leak is enunciated Strengths & Weaknesses The big advantage of the Model Compensated Volume Balance method over non model based methods is that it is capable of fast detection of leaks even in the presence of transients. Multiple leak detection averaging periods give the technique the ability to detect larger leaks very rapidly (usually within a few minutes) and small leaks (down to the sensitivity of the flow meters) within thirty minutes to two hours. The accuracy of the model generated packing rate is not sensitive to the degree to which the model is in tune. Uncertainties in the roughness of the pipeline or the fluid properties have less effect on the packing rate since they tend to affect the computations within a pipeline segment uniformly throughout the segment. If the packing rate is correct, then the leak detection remains valid even when the model is not in tune. This makes the technique valid over a wide range of operating conditions including very low flow rates (a condition which is hard to simulate well due to the sensitivity of flow to very small changes in pressure). The Model Compensated Volume Balance requires all inlet and outlet flows to be metered and pressures and temperatures at the endpoints of all independent pipeline segments. Ultimately, the accuracy of the leak detection depends upon the accuracy of the flow meters. This can make the method more expensive to implement if flow meters have to be added when the leak detection is installed. 4.3 Sensitivity Versus False Alarms The number one complaint with leak detection systems according to a survey conducted last year by the API was that the systems generated too many false alarms. The false alarm rate and the sensitivity are directly related. That is, the higher the sensitivity, the higher the false alarm rate. Most systems installed today have several subsystems or functiona capabilities Pipeline Simulation Interest Group, October

17 Tutorial on Software Based Leak Detection Techniques designed exclusively for the reduction of false alarms. However, due to fluctuations and malfunctions in instrumentation, false alarms can never by completely eliminated. Ultimately, the user must decide the correct trade off between sensitivity and false alarms for his operation. Model based leak detection software should employ methods for the reduction of false alarms. Some of the capabilities designed to reduce false alarms are:. Persistence Requirements. Automatic tuning. Filtering of input data n Qualifying of input data. Dynamically computed leak thresholds Persistence requirements imposed on the issuing of a leak alarm are used to prevent a spike in the data caused by instrumentation error or some other non-hydraulic event from producing a leak alarm in a short duration leak detection time period. The persistence requirement is an integer specifying over how many time periods, scans, or calculations the leak condition must exist before an alarm is generated. Longer leak detection averaging periods are typically used to smooth out the fluctuations in model tracking, thereby increasing the sensitivity of the leak detection. However, averaging periods do not work very well on fluctuations produced by spikes or sharp transients from non-hydraulic sources since they can easily be of a magnitude which is much larger than any physical transient, producing very large errors in the computed results which are difficult to average out. Persistence requirements are effective in dealing with these very short duration non-hydraulic phenomena, but they produce a delay in the issuing of the alarm of a length equal to the persistence multiplied by the scan or time interval. Automatic tuning of the simulator is absolutely crucial to producing model results which are faithful representations of the hydraulics of the pipeline. A tuned model will react more faithfully to transients, producing more reliable results in the leak detection calculations. Simulation models which are given bad data will produce bad results. This axiom may seem obvious, but in a real time installation, a modeling system must be able to effectively deal with less than perfect data without producing unusable results. To the extent that bad data can be removed before it is presented to the model, the leak detection sensitivity and false alarm rate will be vastly improved. For this reason, most model based leak detection systems utilize some form of data filtering on the front end. Some system are further equipped with extensive data analysis capabilities whereby readings from measurements are tested for reasonableness and even corrected before being presented to the model. Pipeline Simulation Interest Group, October

18 Leak detection thresholds are often computed based on the current transient conditions on the pipeline. During slow to moderate transients, the simulation should be able to faithfully track the hydraulics of the pipeline. During very sharp transients, slight uncertainties in the knowledge of tuned parameters and fluid properties can produce short duration fluctuations in the leak detection parameters. By temporarily adjusting the leak detection threshold, false alarms produced by over or undershooting of the leak detection parameters can be avoided. The figure below demonstrates the adjustment in the volume balance threshold when sharp transients are present on the pipeline. The upper graph shows the flow balance and packing rate. The lower graph shows the resulting volume balance and the leak detection threshold. About a third of the way through the data set, an unexplained fluctuation in the flow balance produces a sharp transient in the volume balance. The threshold is adjusted in response to the flow balance fluctuation. When a leak does occur, the threshold adjusts upward temporarily delaying the detection of the leak. However, once the initial sharp transient decays, the threshold is reduced toward its normal level and the leak is declared. With proper setup, dynamically adjustable leak thresholds can dramatically reduce false alarms while retaining target leak detection sensitivities. DYNAMIC THRESHOLD LLI FLOW BAL 4.4 Data Requirements The data necessary for the operation of a model based leak detection system can be categorized as static and dynamic data. Static data refers to the information describing the physical pipeline system including the pipe characteristics and the measurements available on the pipeline. Examples of static data are:. Pipe lengths & diameters Pipeline Simulation Interest Group, October

19 Tutorial on Software Based Leak Detection Techniques. Wall thickness. Valve types and locations. Ground thermal properties. Pipe roughness I Sensor types and locations. Sensor accuracies I Etc. Dynamic pipeline data refers to the information from the instrumentation which is updated in real time and used by the model either for boundary conditions or for calculating leak detection parameters. Examples of dynamic data are:. Pressures. Temperatures. Flows. Fluid compositions (specific gravity or API gravity). Ground temperatures I Sensor qualities. Valve States. Etc. Pressures and temperatures are necessary at all pipe endpoints and at the suction and discharge of pump or compressor stations. Flow measurements are needed for all inlets and outlets of the pipeline. Fluid composition measurements are necessary at all fluid inlets for pipeline where the fluid properties change significantly. Where properties are fairly constant, they can usually be entered as constants rather than through instrumentation in real time. Ground temperatures are needed for pipelines conducting critical fluids such as ethylene, ethane, and propane. 4.5 Costs of Installation The license fees for model based leak detection systems usually account for from 25% to 50% of the total cost of the installed system. The remainder of the cost is spent on the configuration, setup, and tuning effort. In order to configure a leak detection model, the description of the pipeline and the available instrumentation must be entered into the model database. The systems usually provide a great deal of flexibility in the setup of the leak detection parameters. Finally, the system must be brought on-line and monitored for a Pipeline Simulation Interest Group, October

20 Tutorial on SoRware Based Leak Detection Techniques substantial length of time. This monitoring of the system is important in making adjustments to the system to increase the sensitivity and reduce the number of false alarms. Most of the systems available today are relatively easy to configure. However, the amount of information that must be supplied to the model is frequently intimidating to the customer and so little or no effort is made to perform the configuration or installation work with in-house resources. Consequently, most if not all of the work involved with configuring a system properly is performed by the leak detection vendors at normal consulting rates. Many of the advances in leak detection over the last few years have been to make the systems more serviceable by the pipeline companies. _.. Typical costs for vendor supplied leak detection software is seventy five to one hundred thousand dollars. The cost of services required to properly commission the system in the field ranges from one hundred fifty to three hundred thousand dollars. These estimates will vary according to the features desired on the system and the complexity of the pipeline. The above costs are typical for a transmission line of several hundred miles with four or five compressor or pump stations. Pipeline Simulation Interest Group, October