Gas Pipeline Leak Detection - A Tutorial · 2018-04-18 · hydraulics using some (or all) of the real time pipeline measurements as inputs. The RTTM simulation of the pipeline operation

Copyright 2015, Pipeline Simulation Interest Group This paper was prepared for presentation at the PSIG Annual Meeting held in New Orleans, Louisiana, 12 May – 15 May 2015. This paper was selected for presentation by the PSIG Board of Directors following review of information contained in an abstract submitted by the author(s). The material, as presented, does not necessarily reflect any position of the Pipeline Simulation Interest Group, its officers, or members. Papers presented at PSIG meetings are subject to publication review by Editorial Committees of the Pipeline Simulation Interest Group. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of PSIG is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, Pipeline Simulation Interest Group, P.O. Box 22625, Houston, TX 77227, U.S.A., fax 01-713-586-5955.

Abstract

This paper examines the feasibility of Real Time Transient

Model (RTTM) based methods for gas pipeline leak detection,

elucidates the factors that must be managed for effective gas

pipeline leak detection, and examines factors that impact leak

detection and location sensitivity.

Introduction

A growing regulatory focus on minimizing the impacts of

ruptures in natural gas commodity pipelines is increasing the

pressure on the operators of such systems to provide means of

rapidly detecting and locating such leaks. Leak detection

systems have become standardized components of liquid

commodity pipelines over the last few decades, but have not

been emphasized for natural gas systems.

Although many methods have been used to detect leaks in

liquid systems, the most commonly used approach uses real

time transient models and a mass-balance approach to detect

commodity losses. The approach is extensible to gas systems in

a fairly straightforward manner, and this paper will discuss such

implementation. However, it is worth nothing that gas systems

have certain differences that make them distinct from liquid

pipeline systems. One difference is that gas pipelines,

especially if they are part of or support gas distribution, have

the potential to be far more highly networked, branched and

looped than liquid transportation systems. Gas is a far more

1 http://www.politico.com/story/2015/04/the-little-pipeline-

highly compressible commodity than most liquids are, and this

has ramification for desired levels of instrumentation and speed

of response. Finally, gas pipelines are more highly typified by

maintenance requirements that can interfere with or degrade the

performance of RTTM systems.

Another significant different between liquid and gas pipeline

leak detection requirements is that generally, in a liquid line, a

very large leak may be quickly identified by rate of change

alarms. In contrast, a large leak in a gas pipeline, because of

the compressibility of the gas, will cause much slower changes

in the pipeline pressure. A gas pipeline therefore, may need to

rely on an RTTM based leak detection system even for the

reliable and timely detection of very large leaks.

This paper attempts to illuminate these issues and equip the

reader to understand and deal with them.

Regulatory and Public Pressures

While pipelines are considered relatively safe methods of

transporting natural gas, there have been periodic catastrophic

leaks with loss of life and many smaller leaks. That fact,

combined with the aging gas pipeline infrastructure in many

parts of the country is increasing pressure on gas pipeline

operators to install leak detection systems on their pipelines.

After years of hearings and considerations, and in the wake of

the PG&E San Bruno leak in 2010, in 2013, the NTSB issued

the following recommendation to PHMSA:

Require that all operators of natural gas transmission and

distribution pipelines equip their supervisory control and

data acquisition systems with tools to assist in recognizing

and pinpointing the location of leaks, including line breaks;

such tools could include a real-time leak detection system

and appropriately spaced flow and pressure transmitters

along covered transmission lines.

On April 21, 2015, Politico published an article with the

caption, “Pipelines blow up and people die” 1. The lead

picture was taken from AP archives of the 2010 San Bruno

agency-that-couldnt-117147.html

PSIG 1504

RTTM Based Gas Pipeline Leak Detection: A Tutorial Ed Nicholas, Nicholas Simulation Services LLC, Philip Carpenter, Serrano Services, Morgan Henrie, MH Consulting Inc

2 Ed Nicholas, Philip Carpenter, Morgan Henrie PSIG 1504

leak with the caption, “A massive fire roars through a mostly

residential neighborhood following a natural gas pipeline

rupture that killed eight people and injured more than 60 on

Sept. 9, 2010, in San Bruno, California”. The story’s tag line

refers to PHMSA as “The little agency that couldn’t”.

Figure 1 – AP Photo of San Bruno Pipeline Leak1

Interestingly, President Obama, on the same day, proposed 3.5

billion dollars in federal funding as incentives to replace the

aging pipeline system.2

The Politico article included the graphic shown in Figure 4

illustrating the size and location of all gas and liquid pipeline

leaks from 2010 to 2015. The lead in caption was: Explosions,

leaks and spills. There’s more than one leak, failure or rupture

involving an oil or gas pipeline every day in the United States...

Figure 3, Figure 2, and Figure 5 are also taken from the Politco

article and show the deaths, injuries, causes, and costs

associated with pipeline leaks.

While the reporting in some cases may seem alarmist, it isn’t

the authors’ intent to comment on the tone of the reports. The

Politico report does seem to be well researched and makes some

compelling arguments that will resonate in a number of

quarters.

The headlines on April 15, 2015 of a number of papers reported

that PG&E had been fined 1.6 billion dollars as a result of

investigations arising from the 2010 San Bruno leak.

The public, regulatory, and state and federal government

attention and perceptions of hazards associated with natural gas

pipelines is growing. As a consequence, the authors anticipate

that the next 10 years will see increasing expectations for

pipeline companies address risk issues. A consequence of this

2 http://www.politico.com/story/2015/04/aging-gas-pipeline-

is likely to be an increasing pressure to install RTTM based leak

detection systems on natural gas pipelines.

Figure 2 –Causes of Pipeline Leaks1

Figure 3 –Deaths and Injuries1

overhaul-barack-obama-117191.html

PSIG 1504 RTTM Based Gas Pipeline Leak Detection: A Tutorial 3

Figure 4 –Incidents of Pipeline Leaks – 2010 – 20151

Figure 5 –Costs of Incidents: Damage and Penalties (Note reference to Penalties Collected – not Penalties Levied) 1


RTTM Based Leak Detection

Real Time Transient Model (RTTM) based leak detection is a

term used in API 1130 to describe leak detection using a

dynamic model of the pipeline system. In an RTTM approach

a real time model is used to represent the dynamic pipeline

hydraulics using some (or all) of the real time pipeline

measurements as inputs. The RTTM simulation of the pipeline

operation is then used to detect leaks by detecting anomalies

between the pipeline simulation and the measured data. RTTM

methods have historically been applied to liquid pipeline leak

detection and less so to gas pipelines. But, they are increasingly

being used to support leak and rupture detection in gas-phase

pipelines.

Figure 6 provides an overview of the real time processing cycle

of an RTTM based leak detection system. The cycle

components include:

1. Obtain a snapshot of real time data from SCADA

2. Pre-process the real time data with some validation testing

3. Update the RTTM state to the current data time.

4. Analyze the results of the RTTM simulation along with the

real time pipeline measurements to detect and pinpoint

leaks.

This cycle is repeated on a periodic basis (a few seconds to a

minute or two) as new data becomes available, updating the real

time model state and the leak detection analysis with each cycle.

Real Time Data from SCADA

Pressures

Temperatures

Fluid characteristics (perhaps)

Flow rates / volumes

Valve statuses

Engineering Data

Pipeline details

Thermal properties of pipeline surroundings

Fluid properties

RTTM Pipeline Simulation

Compute flows, pressures, temperatures

Track fluid properties through pipeline

Calculate heat loss/gain to/from pipeline surroundings

Slow, automatic adjustment of modeling parameters to

reduce inconsistencies within model or between

model and real time data

Real Time Data Inspection and Validation

Smoothing

Bad data rejection / compensation

Data substitution?

Data Correction?

Leak Detection Analysis

• Analyze raw and validated data and compare to RTTM

calculations

Hypothesis 1: Normal Operations

Hypothesis 2: Probable Leak

• Issue alarm if conditions for accepting leak hypothesis are met

Figure 6 – Overview of RTTM Based Leak Detection

API 1130 doesn’t specifically state how an RTTM system must

operate. However any effective real time transient model must

provide real time solution of the equations describing the

motion and state of the fluid in the pipeline including:

• Continuity (Conservation of Mass)

• Momentum (Newton’s second law)

• Equation of state

• Energy (including heat transfer to pipeline surroundings)

The RTTM’s representation of a pipeline’s real time state

expects that, in the absence of leak, mass is conserved in the

pipeline. Therefore, whether or not a specific vendor’s leak

detection algorithm explicitly is formulated in directly in terms

of conservation of mass, it is a fair generalization to state that

all RTTM based systems are based on the following premise:

In the absence of a leak, mass is conserved in the pipeline. An

RTTM allows one to realistically calculate mass in a pipeline

in real time. A leak can be detected by comparing the

observed rate of change of mass (computed by the RTTM) in a

section of pipeline with the observed mass flow rate into and

out of that section of pipeline.

Fundamental Assertion 1 – Detecting Leak Using

Conservation of Mass

It is much more common to work in volumetric units rather than

mass units on natural gas pipelines. In the US, the standard

volume unit is standard cubic feet (SCF) with flow rate as

standard cubic feet per hour (SCFH) or standard cubic feet per

day (SCFD). Standard conditions are typically considered to be

14.696 psia and 60 degrees F. Common abbreviations also

include MSCFH (thousands of standard cubic feet per hour) and

MMSCFH (millions of standard cubic feet per hour). The

typical SI volumetric unit is Normal m3 where Normal

conditions are a pressure of 101.325 kPa (1.013 bars) and

temperature either 0oC or 15oC.

One generally makes the implicit assumption that the principal

of conservation of mass can be replaced with an equivalent

statement of conservation of standard volume. Making this

assumption, one asserts the following:

In the absence of a leak, standard volume is conserved in the

pipeline. An RTTM allows one to realistically calculate

standard volume (line pack) of a pipeline in real time. A leak

can be detected by comparing the observed rate of change of

standard volume (computed by the RTTM) in a section of

pipeline with the observed standard volumetric flow rate into

and out of that section of pipeline.

Fundamental Assertion 2 – Detecting Leak by

Conservation of Standard Volume

We briefly discuss the validity of converting from the concept

of conservation of mass to conservation of standard volume in

an endnotei.

For the purposes of this paper, we define three fundamental

quantities:

Flow Balance (FB): The sum of all flows into a pipeline or

pipeline section minus all flows out of that pipeline or pipeline

section. This is expressed in standard volumetric flow rate units

(e.g. SCFH).

Packing Rate (PKR): The packing rate is the rate of change of

gas volume in the pipeline. This is expressed in standard

volumetric flow rate units (e.g. SCFH).6


Volume Balance (VB): The Volume Balance is the Flow

Balance minus the Packing Rate. This is expressed in standard

volumetric flow rate units (e.g. SCFH).

VB = FB - PKR

Equation 1: Definition of Volume Balance

In the absence of a leak, one expects the following:

VB = 0 � No Leak

Equation 2: Ideal No Leak Condition

And, in the event of a leak:

VB > 0 = Leak Size (after a little time)

Equation 3: Ideal Leak Condition

These equations are exactly correct under the following

conditions:

• All flows (other than the leak) into and out of the pipeline

section are accurately measured

• Packing rate of the pipeline section can be precisely

calculated by the RTTM.

Of course, neither of these conditions are fully met. There are

always errors in the measurements, limitations to the pipeline

modeling representation, and uncertainties in the physical

parameters and measurements driving the pipeline model. One

typically accounts for the errors in these basic quantities by

some form of thresholding. In the simplest formulation, we

state:

VB > Threshold � Leak

Equation 4: Leak Condition

VB < – Threshold � No Leak

Equation 5: No Leak Condition

-Threshold < VB < Threshold � Impossible to say

Equation 6: Non-Deterministic Condition

Some leak detection software vendors formulate their leak

detection logic precisely in these or equivalent terms. Others

have implemented methods that, while still based on these

principles, don’t specifically define a Flow Balance and

Packing Rate. However, they generally have some concept

equivalent to Volume Balance and some formulation similar to

the three equations above.

Note, however, that the NTSB recommendations propose that a

real time leak detection system be implemented that can assist

in recognizing and pinpointing leaks. Compared to detecting

leaks, pinpointing leaks is significantly more challenging. This

paper will address this particular challenge in more detail.

RTTM Dependency on Accurate Data and Modeling

The performance of an RTTM based leak detection system is

dependent upon the availability of accurate and timely real time

instrumentation and an accurate transient model of the pipeline

system.

All of the following influence the performance of the leak

detection system:

• The placement and accuracy of flow meters

• The placement and accuracy of pressure transmitters

• The placement and accuracy of temperature transmitters

• Real time valve positions

• Accuracy of real time measured or assumed inlet gas

properties

• Accuracy with which one can represent the thermal

properties of the pipeline surroundings including heat

capacity, density, thermal conductivity, and ground

temperature.

• Accuracy of physical pipeline description including pipe

diameters, wall thicknesses, lengths, valve locations, and

elevation profile and nominal roughness.

• The accuracy with which the pipeline model solves the

partial differential equations that describe the dynamic

state of the pipeline.

• The frequency of real time updates (i.e. SCADA scan rate)

and time skew between individual data points

• Resolution of real time data

Finally, the performance of the leak detection system depends

upon the effectiveness of the algorithms provided within a

specific implementation to deal with the uncertainties inherent

in the items listed above.

One may ask, “Aren’t all RTTM based systems basically the

same?” The answer is no. Each supplier must make decisions

about how to deal with the availability and accuracy of data.

Furthermore, there are different ways in which the available

data can be used as inputs to a real time model. This paper

attempts to clarify some of these issues.

Instrumenting a Pipeline for Real Time Leak Detection

A Pipeline Poorly Instrumented for Real Time Leak Detection

Figure 7 illustrates a pipeline poorly instrumented for real time

leak detection. Features that limit leak recognition and

pinpointing include the following:

1. Some supply or delivery flows are not metered in real time.

2. There are no inline flow meters allowing the pipeline to be

easily subdivided for leak detection monitoring.

3. There are long lengths of pipeline without any intermediate

pressure measurements.

4. There are no pressure measurements at some pipeline


branch points.

5. Few temperature measurements are available.

6. Valve positions are not available in real-time.

7. Supply gas compositions are poorly known.

From the perspective of RTTM based leak detection best

practices, it is an unfortunate reality that available

instrumentation on existing gas pipelines often resembles

Figure 7 more than the ideal (Figure 8) which we will discuss

next.

It is important to note that this doesn’t preclude obtaining

benefit from a RTTM based leak detection system. Some

monitoring is much better than no monitoring. However, the

quality, placement, and availability of real time instrumentation

will impact all of the following:

1. The sensitivity of the leak detection system

2. The time required to detect a leak or rupture

3. The ability of the system to reliably detect leaks without

issuing false alarms

4. The accuracy with which the leak or rupture can be pin-

pointed

A Pipeline Well Instrumented for Real Time Leak Detection

In contrast, Figure 8 shows an example of a pipeline well-

instrumented for real time leak detection. Key characteristics

are:

1. Every flow into and out of the system is metered and in

SCADA

2. Inline pipeline flow meters subdivide the larger pipeline

into smaller segments that can be independently monitored

for leaks

3. Pressure is available in real time at frequently spaced

intervals.

4. Pressure is available in real time at all pipeline branch

points.

5. Pressure is available in real time on each side of every

active device in the pipeline including valves, compressors,

and regulators (not shown)

6. The open fraction of every valve that can affect flow paths

in the pipeline is available in real time. Open fraction is

much more useful than simply open, closed, and in-transit

status.

7. Temperature is available in real time at frequently spaced

intervals and for all pipeline supplies and deliveries

8. Though not shown, the gas composition is available in real

time for all supplies unless essentially constant.

9. All real time data is updated by SCADA every few seconds

with high resolution and no deadbands.

Though rarely available, frequent measurement of ground

temperature at pipeline depth a few feet from the pipeline is

useful.


M1

FF1 P

T

PP1

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P3

Compressor

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F F4PPP5 P7

n1 n2 n3 n5 n7n0 n8

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Leg 1 Leg 2 Leg 3 Leg 4

Leg 5 Leg 6

Leg 10

Leg 11

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F F8PP16

n16 n17Leg 9

Valve 1

n13

Delivery not

metered in SCADA

Valve status not in

SCADA

Branching without pressure

transmitter nearby

Branching without pressure

transmitter nearby

Long pipeline length without

intermediate pressure

Minimal temperature

instrumentation

Delivery not

metered in SCADA

Figure 7 – Example of Pipeline Poorly Instrumented for Leak Detection

M1

FF1 P

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F2

Compressor

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Leg 1 Leg 2 Leg 3 Leg 4

Leg 5 Leg 6 Leg 8

Leg 10

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FF6

Valve 1 V1

P P12

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Leg 7

n13

• All supplies and deliveries metered and in real time

• Frequent pressure and temperature transmitters

• P & T at all branch points

• All valve positions that impact flow paths in SCADA

• Inline flow meters allow pipeline to be sub-divided for leak detection

Figure 8 – Example of Pipeline Well Instrumented for Leak Detection


RTTM Approaches (Modeling)

This section discusses approaches to using the available real

time pipeline instrumentation data as inputs to a transient model

which facilitates its use as a tool for detection leaks.

Figure 9 on the next page shows a sample pipeline and

instrumentation. Using this sample pipeline, we will discuss

some different ways with which the available data can be used

to model the pipeline in real time.

Note that we use some common pipeline modeling terms in this

figure. A Leg typically refers to a straight, non-branching

length of pipeline that is often modeled as a single entity. A

Node typically represents a connection point between pipeline

elements and in this drawing are numbered n1, n2 …

Unified Pipeline Model Approach

One approach, illustrated in Figure 10, we term the Unified

Pipeline Model approach. In this approach, none of the dimmed

measurements are used as boundary conditions by the real time

transient model. Instead, the pipeline is modeled as a unified

system using only the measurements that one could actually use

to control the real pipeline as inputs. Since one can’t control

intermediate pipeline pressures such as those at nodes n2, and

n6, these cannot be used as inputs in this modeling paradigm.

And, at the compressor, one has the option of only one of the

following:

1. Direct compressor (e.g. RPM) control

2. Suction pressure control

3. Discharge pressure control

4. Flow control

At supplies and deliveries, either flow or pressure can be

controlled, but not both.

The benefit of this approach is that it assures that flows will

balance throughout the pipeline model. Since the pipeline is

modeled as a unified whole, conservation of mass is imposed

throughout the modeled pipeline. This approach can be useful

for fluid property and scraper tracking.

Unfortunately, this approach to real time modeling is not very

well suited to a Volume Balance approach to leak detection.

However, there have been those who have created RTTM based

leak detection systems using this modeling approach by

examining differences between model computed pressures and

measured pressures and similarly between modeled flows and

measured flows at the points where these are not imposed as

boundary conditions.

Segmented Pipeline Model Approach

Another approach, illustrated in Figure 11, models the pipeline

as small networks bounded by the nearest pressure

measurements. We term this the Segmented Pipeline Model

approach. In this modeling paradigm, Leg 1 would be modeled

using P1 and P2 as upstream and downstream boundary

conditions. Assuming that flow is from left to right, T1 would

be imposed as an upstream temperature boundary condition.

Leg 2 would be modeled using P2, T2, and P3 as boundary

conditions and Legs 3 and 4 similarly.

Note that for the poorly instrumented pipeline example shown

in Figure 7, Legs 1, 2, 10, 11, 5 and 6 would have to be modeled

as a single sub-network as the nearest bounding pressure

measurements are at nodes n1, n3, n10, and n13.

The proponents of this modeling paradigm argue that from a

leak detection perspective, one will obtain the best estimate of

pipeline packing rate from a model that uses all available

pressures and temperatures as boundary conditions. They

would argue that this is the fundamental purpose of the model.

Then, since flow measurements can be used to compute the

flow balance, one then has the best possible representation of

Volume Balance to use as a leak detection signal.

Of course, in this approach, the flows will not balance across

nodes since each pressure bounded subnetwork is modeled as

an independent unit.

For the purposes of this paper, we will refer to the imbalances

across a node as the Node Flow Discrepancy. These values are

of use in leak location calculations. In addition, one can

demonstrate that for any section of the pipeline:

Volume Balance = Sum of Node Flow Discrepancies

Equation 7: Computing Volume Balance from Node Flow

Descrepancies

Note that we use the term Node a little loosely here. In fact,

being more precise, we really mean a one or more nodes that

are bounded by measured or modeled flows into and out of the

collection of nodes. For example, the Flow Discrepancy of the

collection of nodes n4 and n5 is computed from the flow of M2

(into n4) minus the flow at the n5 end of Leg 3 computed by the

RTTM. But for the sake of simplicity, we will continue to use

the term Node Flow Discrepancy in this looser sense where we

sometimes mean a collection of nodes rather than a single node.

It is worth noting that this method also allows for leak detection

to be performed for internal subsets or portions of the pipeline

system. This is possible because the sum of all of the

discrepancies for any subset of the pipeline system reflect the

volume balance of the portion of the pipeline as long as all of

node flow discrepancies for the target portion of the system are

included.

RTTM systems that use this approach typically use a secondary

approach to move fluid properties through the pipeline in a way

that conserves mass in the fluid tracking. There are at least two

different methodologies in use:

1. Run two models in parallel, one implemented using the

Unified Model approach that is responsible for fluid

movement. The second, implemented as a Segmented

Model is responsible for computing packing rate for the

leak detection calculations. Comparisons between the two


models can be used to adjust modeling parameters such as

pipe roughness.

2. A post-processing state estimation step. In this approach,

with each modeling pass through the pipeline, the state

estimator calculates a correction to every modeled and

measured flow that minimizes, for example, the sum of the

squares of the flow corrections after normalizing each

correction by the expected error inherent in that particular

flow. These corrections are constrained such that the

corrected flows balance across every node in the pipeline

system. These state estimated flows are then used to move

fluid properties through the pipeline system. The flow

corrections can also be used to adjust modeling parameters

such as pipe roughness.

Unified Pipeline Model with Extra Degrees of Freedom Approach

Figure 12 illustrates a third approach, which, for want of a better

term, we refer to as a Unified Model with Extra Degrees of

Freedom. Similar to the Unified Model, the pipeline is treated

as a whole. However, the constraint of mass balance across

every node is released and instead replaced by an artificial

delivery or supply at every node. And, instead of imposing any

of the pipeline pressures or flows directly as boundary

conditions to the model, the prior to imposing them as boundary

conditions, the software computes an adjustment to every flow

and pressure measurement. So, in the example of Figure 12, a

set of adjusted values F1’, F2’, F3’, P1’, P2’, P3’, P5’, P7’ are

computed and applied to the pipeline model along with five

artificial flows that can be into or out of the pipeline at nodes

n1, n2, n3, n5, and n7. In this approach, one computes the set

of measurement corrections using an optimization technique

that is designed to minimize a weighted sum of all of the

corrections (or corrections squared) while at the same time

attempting to reduce the magnitudes of the extra flows (e.g. by

minimizing a weighted sum of the squares of the extra flows).

The Extra Flows, are essential the Node Flow Discrepancies

described in the Segmented Model approach except that they

are computed after applying the corrections to the measured

pressures and flows.

The sum of the Extra Flows is in fact the volume balance that

remains after applying the computed corrections to the

pressures and flow meters. This sum is typically used as the

leak detection signal.

Note that this modeling approach does not ensure that fluid

properties move through the pipeline without some loss or gain

as fluid can be extracted or injected into the pipeline artificially

by the Extra Flows. Consequently some secondary state

estimation approach or secondary model may be required to

provide accurate fluid property tracking through the pipeline.


M1

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Figure 9 – Sample Pipeline

M1

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FP1

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n6Leg 1 Leg 2 Leg 3 Leg 4

RPM

Compressor

Figure 10 – Unified Pipeline Model Approach

M1

FF1 P

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M4

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F4


Figure 11 – Segmented Pipeline Model Approach

M1

FF1' P

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P P

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M2

FP1'

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P2' P3'

T3T T2

F2'

Compressor

M3

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M4

F

F4'


Extra Flow 1 Extra Flow 2 Extra Flow 3 Extra Flow 5 Extra Flow 7

Figure 12 – Unified Pipeline Model with Extra Degrees of Freedom Approach


Gas Pipeline Response to a Leak

In this section, we illustrate how a pipeline will respond to a

leak under different flow rates. For gas pipelines, the flow rate

fundamentally affects the propagation of the leak event. We

will illustrate by example that the impact of the flow rate on the

propagation of pressure effects resulting from a leak is very

pronounced. However, before turning to the examples, it is

useful to discuss the differences between Liquid and Gas

Pipeline RTTM Systems.

Differences between Liquid and Gas Pipeline Systems

The RTTM approach described above is equally applicable to

liquid and gas phase commodity pipelines. Technically, the

only differences in application are the equations of state used

for the application and any related property differences. The

implementation of the conservation of mass, momentum and

energy equations should in principle be the same.

In practice, the actual implementations of the conservation

equations may be different between gas and liquid commodity

systems because the compressibility or other properties of the

gas may make it more useful to express the conservation

equations in a different form than that used for more

incompressible liquid commodity systems. In addition, certain

nonlinearities characteristic of liquid systems (in particular,

slack line flow, where both liquid and vapor components of the

fluid exist in equilibrium) do not have to be addressed in pure

gas-phase pipeline systems.

In terms of evaluating the response of the system to a leak or

rupture, we note the following:

1. Liquid commodities for pipelines operating in a tight (non-

slack) state are minimally compressible. Disturbances

created by the leak will propagate rapidly to nearby

instrumented locations and packing effects will be smaller.

Consequently, a liquid pipeline in a tight condition is more

tolerant of packing calculation errors and will respond

rapidly to the leak. These systems are also more tolerant of

somewhat lower instrumentation (especially pressure

measurement) densities

2. However, liquid pipelines in a slack condition are highly

compressible. Disturbances created by the leak will

propagate slowly to nearby instrumented locations and

packing effects will be very important. Therefore, a liquid

pipeline in a slack condition requires good modeling and

minimization of packing errors. The leak detection system

will respond slowly to the leak unless a high density of

instruments is provided.

3. Gas commodities are highly compressible. Disturbances

created by a rupture or leak will propagate more slowly to

nearby instrumented locations and packing effects will be

very important. The wave propagation speeds in gas

commodities are typically on the order of 30 to 40% of the

speeds in liquid commodities. Therefore, a natural gas

RTTM requires good real time modeling and minimization

of packing errors. It will respond slowly to the leak. Higher

instrumentation densities are preferred.

The consequences of these issues is explored for gas

commodity pipelines in the following sections.

Gas Pipeline Response to Leak - Examples

For these examples, we use the following simple pipeline case:

Diameter and length: 34 inch, 120 miles

Upstream control: Flow

Downstream control: Pressure

Leak location: Mile 60 (pipeline midpoint)

Leak sizes: 5 and 25 mmscfh

Pressure at Leak: 700 psi

Temperature at Leak: About 60 deg F

Pipeline Flow Rate: 0, 10, 25, and mmscfh

Elevation: Flat

Note that the 25 mmscfh leak is of the order of 100% of pipeline

flow rate. Examining a large leak allows us to clearly visualize

the pipeline behavior. For clarity and simplicity, the pipeline is

in steady state at leak onset and the only transients are those

generated by the leak. We examine leaks on initial flow rates

of 0, 10, 25, and 35 mmscfh.

In all cases, we have assumed that the upstream boundary flow

is controlled at the steady pipeline flow rate and that the

downstream pressure is controlled at the steady pipeline

pressure. For each different initial flow rate case, the

downstream pressure and upstream temperature were adjusted

so that the pressure at the leak site was 700 psi and 60 deg F to

maintain similarity between cases.

In the sections below we examine the pressure profiles that

would exist in the pipeline following a leak event:

25 mmscfh Leak on Zero Initial Flow

Figure 13 shows the pressure profile every 2 minutes for the

first 10 minutes following the onset of a 25 mmscfh leak. The

leak rapidly impacts the entire pipeline, with pressure effects

moving away from the leak site at the speed of sound and with

a noticeable wave front, at least in the first four minutes after

the leak onset at which time the effects have almost propagated

to the upstream and downstream pipeline end points.

Figure 14 is a companion plot to Figure 13. It shows the rate of

change of pressure (psia / min) profiles every two minutes for

the first 10 minutes after leak onset representing the rarefaction

wave propagating from the leak site. In this case, the rarefaction

wave is pronounced and persists even 8 minutes after leak

onset. Of hydraulic interest, illustrated in the 6 and 8 minute

profiles is the fact that reflection from a pressure boundary

(downstream) causes the wave to reverse polarity.

While this paper is not about rarefaction wave leak detection, it

is interesting to observe that such technology could possibly be

well suited for rupture detection on a gas pipeline with zero


flow rate. At higher flow rates the picture is less clear as we

will see.

25 mmscfh Leak on 10 mmscfh Initial Flow


first 10 minutes following the onset of a 25 mmscfh leak. Note

that the leak rapidly impacts the entire pipeline, with pressure

effects moving away from the leak site at the speed of sound

and with a slightly noticeable wave front 2 minutes after the

leak onset. However, the effects are significantly more

localized than in the 0 initial flow case with a greater pressure

drop near the leak site and significantly smaller pressure drops

at the upstream end. After 10 minutes, the pressure at the

upstream end has only dropped 3 psia compared to 11 psia for

the 0 initial flow case.



the first 10 minutes after leak onset. Note that the rarefaction

wave propagating from the leak site is much less pronounced

than in the 0 flow case. Note also, that the rarefaction wave has

much greater attenuation when moving upstream than when

moving downstream.

25 mmscfh Leak on 25 mmscfh Initial Flow


first 10 minutes following the onset of a 25 mmscfh leak.

Compared to the lower flow cases, the leak effects are far more

localized. After 10 minutes, the impact at the pipeline

endpoints, 60 miles away, is virtually zero. Even 40 miles way,

the effects are quite small.



the first 10 minutes after leak onset. Note that the rarefaction

wave is nearly invisible 30 miles from the leak site.

An Aside on Artificial vs Real Thermal Effects

It is interesting to observe the thermal effects in the pipeline

following a large leak as they influence both the real and the

RTTM behavior following a leak. Figure 19 shows what the

authors’ believe are a result of reasonable modeling of the

temperature profiles in the pipeline following a leak. In the

authors’ model, the ground surrounding the pipeline is modeled

as a series of 12 concentric shells of earth with transient

simulation of heat flow through these shells. In contrast, a

simplified RTTM may model the ground by a heat transfer

coefficient with no modeling of heating and cooling of the

pipeline surroundings. The latter approach, from the

perspective of fast transients and the near term effects after leak

onset, is nearly adiabatic in that little heat would be lost or

gained in the first 10 minutes following the leak. Figure 20

shows the temperature profiles following the leak assuming

adiabatic conditions.

There are significant differences between the two different

models. In the former, the temperature drop resulting from the

expansion of the gas is about 2 degrees and in the latter over 16

degrees. The RTTM pipeline simulation is very greatly

affected by the thermal modeling assumptions and, of course,

the knowledge of the thermal properties of the pipeline

surroundings.

Consequently, RTTM best practices require a transient thermal

model of the pipeline surroundings and that significant attention

is given to configuring and tuning this model to provide close

correspondence between observed and modeled temperature

profiles, especially during large pressure transients.

Figure 13 - Pressure Profiles Following 25 mmscfh Leak on 0 mmscfh Pipeline Flow


Figure 14 – dPdt (psi / min): 25 mmscfh Leak on 0 mmscfh Pipeline Flow


Figure 15 - Pressure Profiles: 25 mmscfh Leak on 10 mmscfh Pipeline Flow



Figure 17 - Pressure Profiles: 25 mmscfh Leak on 25 mmscfh Pipeline Flow



Figure 19 – Temperature Profiles following 25 mmscfh Leak on 25 mmscfh Pipeline Flow (transient thermal modeling of

ground)

Figure 20 – Temperature Profiles following 25 mmscfh Leak on 25 mmscfh Pipeline Flow (adiabatic assumption – similar to

simple heat loss coefficient model)


The Leak Signal and Impact of Pressure Transmitter Spacing

Having examined some aspects of leak propagation through a

simple pipeline in the preceding section, we now consider how

this same leaks on the same pipeline could be detected at the

nearest pressure measurements bounding the leak site under

ideal conditions. By ideal conditions, we mean all of the

following:

1. Very accurate measurement of pipeline pressures,

temperatures, and flows.

2. Very accurate knowledge of fluid properties.

3. Availability of all measurement data in real time (as

quickly as we want it).

4. Very accurate knowledge of thermal properties of

pipeline surroundings (thermal conductivity, density,

specific heat, and ambient temperature).

5. Perfect RTTM solution of equations describing

transient pipeline state.

The one departure from ideality that we assume in this section

is the discrete spacing of pipeline pressure measurements. In

particular, we examine how the spacing of pressure

measurements, which are typically much more closely spaced

than flow measurements, impact the RTTM detection of

pipeline leaks.

We will illustrate that the leak signals observed by an RTTM

are greatly influenced by the distance between the leak site and

the pressure transmitters bounding the leak and also on the

underlying pipeline flow rate.

In this section, we consider a leak midway between the nearest

bounding pressure transmitters and vary the transmitter spacing

to explore the ability of an RTTM to detect a leak.

Prior to examine the results of simulations, we pose

Fundamental Assertion 3.

Generally, an RTTM, having no prior knowledge of the leak

event, does not model the leak behavior between bounding

pressure measurements. Instead, it reacts to the changes

observed at the pressure measurements bounding a leak.

Fundamental Assertion 3

The RTTM cannot actual observe the onset of a leak unless

there is a pressure measurement precisely located at the leak

site. Instead, the RTTM observes the impact of the leak at the

pressures measurements most closely bounding the leak. In

fact, the leak causes a wave to travel upstream and downstream.

However, the RTTM, having only pressures at discrete

locations sees the leak as a perturbation at the measurement

location. And, since most RTTMs impose observed pressures

as boundary conditions on the sections of the pipeline bounded

by the pressure measurement, the model reacts by sending a

wave both outward (away from the leak site) and inward

(towards the leak site). Given the ideal conditions we are

assuming here, the outwardly moving wave perfectly represents

the true transient in the pipeline. However, the inwardly moving

wave is a false transient that is introduced by the fact that the

RTTM is initially is unaware of the fact that it was caused by a

leak.

Over time, the discrepancy between the model assumptions (no

leak) and the real pipeline condition (leak present) will result in

a signal that may be identified by the RTTM as a leak. The

artificial wave-fronts associated with the leak onset decay over

time. But in the short term, the artificially induced inwardly

moving wave-front is observable when pressure measurements

are closely spaced or the flow rate is very low compared to leak

size.

The Leak Signal

Most RTTM methodologies result in a signal that represents the

volume imbalance caused by a leak.

Given accurate measurements, accurate knowledge of the

pipeline and it surroundings, and an accurate RTTM, the

observed volume imbalance resulting from a leak can be

computed as the sum of the node flow discrepancies at the

pressures measurements bounding the leak. Over time, the

observed volume balance approaches the actual leak size.


We will not attempt to prove the assertion above but it is a

consequence of the fact that, in the ideal world, the pipeline

would be model perfectly except in the subnetwork bounded by

the nearest pressure measurements spanning the leak site.

Note, however, that for the Unified Modeling Approach with

Extra Degrees of Freedom, Fundamental Assertion 4 will be

violated as the a priori correction of measured flows and

pressures will force some distribution of the leak signal

upstream and downstream of the nodes bounding the leak.

If the leak is in a straight section of pipeline, the observed

volume imbalance is the sum of the node flow discrepancy at

the upstream end of the leg spanning the leak and the node flow

discrepancy at the downstream end of that leg.

For example, for the sample pipeline of Figure 11, if the leak

were in Leg 2, the volume balance would be equal to the sum

of the node flow discrepancies at nodes 2 and 3. The node flow

discrepancy at Node 2 would be computed as the flow in Leg 1

into node 2 minus the flow in Leg 2 leaving node 2. Similarly,

the node flow discrepancy at node 3 would be the flow in Leg

2 into node 3 minus the flow F2 (meter 2) leaving node 3.

For the less well instrumented pipeline of Figure 7, the volume

imbalance caused by a leak in Legs 1, 2, 5, 6, 10, or 11 is

observed as the sum of the node flow imbalances at nodes 1, 3

10 and 13.


One can consider each of the node flow discrepancies at the

pressure measurements bounding the leak as part of the leak

signal. However, the total volume imbalance is the sum of these

and therefore in this section we will consider the observed

volume imbalance (sum of the upstream and downstream node

flow discrepancies) as the leak signal. The volume imbalance

signal does not immediately appear as a volume imbalance of

the size of the leak. But over a period of time, dependent upon

the distance between the leak site and the bounding pressures,

the volume imbalance will approach the leak size.

Observations from Different Measurement Spacing

Figure 21 shows the volume imbalance signals observed from

a 5 mmscfh leak imposed on an initial steady flow of 25

mmscfh. As one would expect the leak is not observed at the

bounding pressure measurements until enough time has elapsed

for a sound wave to travel from the leak to the nearest pressure

measurement.

From Figure 14 we can calculate the observed speed of sound

to be 1196 ft/s as the rarefaction wave associated with leak

onset propagates 82 miles in 6 minutes (60 miles to the end and

back another 22 miles).

In this case, since the leak is midway between measurements,

we expect the following delays before the leak will be observed

at the following distances from leak to measurement (half of the

measurement spacing):

Distance from

Measurement

Speed of Sound

Time Delay

2 Miles 4.5 seconds

4 Miles 18 seconds

8 Miles 35 seconds

12 Miles 52 seconds

16 Miles 70 seconds

20 Miles 98 seconds

Table 1 - Speed of Sound Delays on Leak Signal Onset

Figure 21 exhibits this delay. However, one notes that only the

initial onset of the leak is observed in this time. Considering

the rise time to be the time to rise to 1 – 1 / e (63.2%) of the leak

size (similar to a capacitor charging through a resistor), we

compute the following rise times at different distances from

leak to measurement as:

Distance from

Measurement

Leak Signal Rise

Time

2 Miles 4 seconds

4 Miles 4 seconds

8 Miles 19 seconds

12 Miles 74 seconds

16 Miles 154 seconds

20 Miles 256 seconds

Table 2 – Leak Signal Rise Times – 5 mmscfh Leak on 25

mmscfh Flow

Adding the speed of sound delay and the rise times together,

one observes that it takes nearly six minutes for 63.2% of this

leak t to be observed in the volume imbalances obtained from

the two bounding pressure measurements 20 miles upstream

and downstream. One may also note the following:

• For measurement spacing’s of 4 and 8 miles, there is a

noticeable artificial “wave” in the leak signal that dies out

in less than two minutes. This artificial wave is due to the

model assuming that the pressure transient originates at the

measurement site whereas it originates at the leak site.

• The signal rise time increases with increasing instrument

spacing at a rate that is more closely proportional to the

square of the instrument spacing than to linear.

Figure 22 shows the volume imbalance signals observed from

a 25 mmscfh leak imposed on an initial steady flow of 25

mmscfh. Comparing this very large leak to the more moderate

5 mmscfh leak, we observe that the leak signals observed from

the 25 mmscfh leak are very similar to those observed from the

5 mmscfh leak when one adjusts for the leak magnitude

differences except for the following:

• The artificial wave observed in the 4 and 8 mile spacing’s

is a little less pronounced

• The leak signal rise times are a little longer

Figure 23 shows a 25 mmscfh signal but this time imposed on

a 35 mmscfh initial flow rate. The signal is very similar to

Figure 22 but again, with the artificial wave a little less

pronounced and the leak signal rise times even longer. In this

case, it takes about 8 minutes for the 63.2% of the leak to be

observed in the volume imbalances obtained from two pressure

measurements 20 miles upstream and downstream.

Large, Persistent Artificial Waves at Low Flow

Rates

We know turn to Figure 24 and see a very different behavior.

This figure shows the leak signal from a 25 mmscfh leak when

the initial pipeline flow is 0. In this case we observe the

following:

• The rise time of the leak signal is nearly instantaneous once

the signal, moving at the speed of sound, reaches the

bounding pressure measurements.

• The artificial waves induced by the assumption that the

signal originates at the pressure measurements rather than

at the midpoint between the pressure measurements is

pronounced, even at 32 mile pressure measurement spacing

and endures for more than 10 minutes.

The primary attenuation mechanism is friction. Since frictional

losses are proportional to the square of the velocity, we would

anticipate that they would be substantially less in the event of a


much smaller leak. Therefore, for small leaks on a static

pipeline, one might observe artificial waves persisting for a

much longer time, even with more widely spaced pressure

transmitters. In short, the artificial transient is not a result of

the large leak size.

Can One Suppress the Artificial Waves?

While having not tested this hypothesis, we believe that the

artificial waves can be substantially suppressed by imposing a

filter on the measured pressures. Rapidly changing pressures

result in the high magnitude waves that reflect back and forth

between the ends of the subnetwork spanning the leak site.

Therefore, if one imposes a digital filter on the input data, prior

to feeding it to the model, whose cut-off frequency’s

wavelength is somewhat greater than the length of the longest

length between the bounding pressure measurements, one

would expect that it the artificial waves would be eliminated

since the frequencies remaining in the input data would be too

low to generate an artificial wave in the subnetwork. On the

other hand, this would substantially impact the speed of

response of the pipeline to real transients. Still, this is likely the

best approach though further evaluation and testing would be

appropriate.

Under this proposal, for measurements spaced 20 miles apart, a

filter with a cutoff frequency of 0.01 HZ or lower would be

appropriate.


Figure 21 – Observed Volume Imbalance Dependence on Pressure Spacing – 5 mmscfh Leak on 25 mmscfh initial flow






Estimating Leak Location

An RTTM can assist in pinpointing the location of a leak on the

pipeline. In this section we discuss, from moving from the most

gross to the most precise approach, the three levels of location

capability that can be provided

Bracketing Leaks using Bounding Flow Meters

An RTTM based leak detection system can usually reliably

locate a leak between the nearest bounding flow meters. This

is because the system relies on identifying imbalances in the

system that violate the law of mass conservation. In-line flow

meters provide firm boundaries between which these

calculations can be performed. Therefore, in-line flow meters

can be useful for segmenting the pipeline into “leak detection

sections” that allow one to localize a leak to one of the sections.

Note however, this is reliable only when the leak size is large

compared to the total uncertainty inherent in all flows entering

or leaving the subnetwork.

Bracketing Leak using Local Flow Discrepancies

As we discussed previously, in the ideal (perfect model and

perfect and timely data), the only flow discrepancies would be

at nodes bounding the pipeline leg or subnetwork spanning the

leak site. In the extreme, for a leak immediately upstream or

downstream of a node, the only flow discrepancy that will be

observed is at that node adjoining the leak.

Therefore, an RTTM can further localize a leak by examining

every pipeline subnetwork bounded by pressure measurements.

If there is a leak in the pipeline, it can be localized to the

pipeline subnetwork for which the sum of the bounding node

flow discrepancies is largest. Note however, that a variety of

errors can influence the node flow discrepancies. Therefore,

this is only reliable when the leak size is large compared to the

errors inherent in the flow discrepancies.

Also, note that a leak near a pressure measurement will cause a

flow discrepancy primarily at that node. Therefore, for a leak

near a pressure measurement, it is often difficult to determine

whether the leak is upstream or downstream of that

measurement.

Pinpointing Leak by Detecting the Time at which the Leak Signal Arrives at the Bounding Pressure Measurements

By detecting the precise times at which the node flow

discrepancies bounding a leak detect the leak onset, one might

be able to localize the leak based on the different arrival times

of the leak signal at the bounding nodes. This could be very

accurate if instrumentation is closely spaced, the data

acquisition rates are very fast, and the leak is large enough to

generate a rarefaction front that is detectable using pipeline

pressure transmitters. It is rare that all of these conditions are

met. Therefore, while this is an interesting area for further

investigation, we will not consider it further in this discussion.

Pinpointing Leaks using Flow Discrepancies

If the RTTM has been successful in localizing a leak to a

pipeline subnetwork bounded by pressure measurement, the

RTTM may be able to pinpoint a leak within the subnetwork

more precisely by examining the magnitudes of the node flow

discrepancies bounding the leak.

The node flow discrepancies are good detectors of the “kinks”

in the pressure gradient that are caused by a leak.

First, we examine the pressure profiles observed by an RTTM

and compare those to the true pressure profile in a pipeline for

the case of a 25 mmscfh leak imposed on an initial steady

pipeline flow of 10 mmscfh for the sample 120 mile pipeline

we have used in previous sections.

Figure 25 shows the “true” pressure profiles (those generated

by a simulation of the pipeline following a leak) immediately

prior to leak onset and at 2, 6, and 10 minutes after leak onset.

As one would expect, there is a “kink” in the pressure profile at

the leak site.

Figure 26 shows the pressure profiles computed by the RTTM

with pressure measurements spaced every 4 miles and the leak

midway between the measurements. Unlike the true profile, the

kinks are observed at the nearest pressure measurement sites:

mile 58 and mile 62.

Figure 27 the pressure profiles computed by the RTTM with

pressure measurements spaced every 24 miles and the leak

midway between the measurements. The kinks in the pressure

profiles are observed at the nearest pressure measurement sites:

mile 48 and mile 72.

The task of the RTTM is to detect these kinks, and to use them

to estimate the true leak location located somewhere between

the bounding nodes.

The node flow discrepancies embody all of the knowledge

about the inconsistencies in the pressure profiles at the

bounding nodes, they become the natural candidate for

pinpointing the pipeline leak.

The classical approach to pinpointing leaks in liquid pipelines

assumes that there is an upstream and downstream pressure

measurement bounding the leak and that the pipe does not

branch between the measurements. In this case, one can

demonstrate that for leak flow rates that are small compared to

the pipeline flow rate, the following is true for incompressible

fluids:

dn

up dn

FDFractionalLocation

FD FD=

+

Equation 8 – Classical Fractional Leak Location Equation


where FractionalLocation is the leak location expressed as a

fraction of the length between the upstream and downstream

bounding nodes, upFD is the flow discrepancy at the upstream

node and dnFD is the flow discrepancy at the downstream

node. When the leak is at the upstream node, all of the flow

discrepancy would be at the upstream node so 0dnFD = and

FractionalLocation is zero as one would expect. On the other

hand, if the leak is at the downstream node, all of the flow

discrepancy is there and the fractional location computed by

this equation is 1.

So, this equation meets the reasonability test and one would

expect this equation to represent the limit cases equally well on

a gas pipeline.

But, the derivation of Equation 8 (which we will not address in

this paper) makes a number of assumptions that are not

necessarily valid for gas pipelines. We demonstrate the

limitation of this equation for gas pipelines through RTTM

simulation examples.

Since it will turn out that Equation 8 does not very well for gas

pipelines, from this point forward, we will refer to this simply

as the Leak Proximity Signal.

dn

up dn

FDProximitySignal

FD FD=

+

Equation 9 – Definition of Leak Proximity Signal

Turning again to the example 120 mile pipeline of our previous

examples, we compute the proximity signal for various cases.

Figure 28 Figure 29 and Figure 30 show the leak proximity

signal observed for a leak of 25 mmscfh imposed on initial

steady flow rates of 0, 10, and 25 mmscfh respectively for a

pressure measurement spacing of 36 miles. In each case, we

compute the signals observed for a leak at mile 60 located at

different fractional locations between the upstream and

downstream pressure measurements. To do this, we varied the

upstream and downstream pressure measurement locations,

keeping the spacing at 36 miles but changing the proximity of

the measurements to the leak site. For these 4 different

fractional locations, the measurement locations are shown in

Table 3 below.

True Leak Location

Fraction

Upstream

Node MP

Downstream

Node MP

0.25 51 87

0.50 44 78

0.75 33 69

1 24 60

Table 3 – Measurement Locations for Different Location

Fractions – 36 mile Pressure Spacing

We note the following:

• The leak proximity signal does not reliably pinpoint the

leak. For example, for the 0.25 true fractional location, 15

minutes after leak onset, it has the values of 0.36, 0.52, and

0.30 for the three different cases.

• In the 0 initial flow case, in the early period following the

leak, we see a pronounced influence of the artificial wave

noted in previous sections

• In the 0 initial flow case, in the early period following the

leak, we see a pronounced influence of the artificial wave

noted in previous sections

• It takes several minutes for the proximity signal to

approach its end value in the second and third cases.

• In the 0 flow case, the proximity signal for the 0.5

fractional leak location is drifting downwards even after 15

minutes. We presume this is because the flow rate in the

pipeline is adjusting to the new conditions and the value of

the proximity signal is affected by the pipeline flow.

Figure 31 shows the leak proximity signal for the 0 initial flow

case with an 8 mile measurement spacing. Comparing this to

Figure 28, we observe the following:

• With the 8 mile spacing, the leak proximity signal shows

more influence of the artificial wave within the Leg

spanning the leak discussed earlier.

• The downward trend of the proximity signal associated

with the 0.5 leak location fraction is more pronounced with

the 8 mile spacing than with the 36 mile spacing.

• Otherwise, the two figures are similar.

Figure 32 and Figure 33 show the underlying node flow

discrepancies from which the proximity signal was calculated

for the 8 mile measurement spacing for two of the cases, the

0.25 and the 0.5 fractional leak locations.

Most interesting is the 0.5 case. From it we note the following:

• While the leak proximity signal of Figure 31 seemed fixed

at 0.5 for the first several minutes following the leak onset,

this is only because the impact of the artificial waves were

observed at both the upstream and downstream end at

precisely the same time. In fact, both node flow

discrepancies were oscillating for several minutes.

• At about 9 minutes into the leak, the upstream and

downstream node flow discrepancies begin to diverge from

each other. This time corresponds to the time for the leak

to event to propagate to the pipeline endpoints and back to

the pressure measurements. At this time, the pipeline

begins to stabilize to a different flow and the proximity

signal changes value.

The Difficulty of Pinpointing a Leak in Pipelines with Cross-overs or Branches

All of the discussion and investigation above about the leak

proximity signals has focused on the case in which the leak is


in an unbranching pipe bounded by an upstream and

downstream pressure measurement. If there are branch points

or cross-overs that are not bounded by pressure measurements,

pinpointing a leak to the proper leg or branch of the subnetwork

spanning the leak can be very difficult. This is not a problem

for which there is an easy resolution. Therefore, if pinpointing

leak location is desired, it is necessary to provide pressure

measurements at all pipeline branch points.

Is there a Better Leak Proximity Signal?

We have demonstrated the dependence of the proximity signal

on pipeline flow rates. And, since pipelines are rarely in steady

state, one can expect that the proximity signal will shift

throughout a leak event simple due to normal transients

propagating through the pipeline. The leak proximity signal

therefore provides only a rough estimate of leak location.

Therefore, even in the ideal case of excellent and accurate data

and modeling, the proximity signal will have inherent errors.

One way to reduce these errors is to provide more closely

spaced pressure measurements. While the error inherent in the

proximity signal will not be reduced, the error in the estimated

leak location will be reduced proportional to the reduction in

measurement spacing.

Another approach that would likely work well would be to

perform a number of RTTM simulations once a leak is detected,

imposing the leak at various locations and times and iterating to

a leak location and time that best explains the observed flow

discrepancy trends. However, to the authors’ knowledge, this

type of capability is not available in any current leak detection

system. We assert the following:

Pinpointing even a very large leak to a precise milepost in a

gas system is difficult. Without a somewhat more

sophisticated approach than that offered by Equation 8 we

expect that one may be only be able to reliably locate a leak to

perhaps 30% of the distance between bounding pressures.

Until RTTM systems offer a more advanced leak location

estimation approach, precise location estimation may require

more closely spaced pressure transmitters.


The Influence of Real Pipeline Conditions on Leak Location Estimation

One should be aware of the fact that pinpointing a leak is far

more difficult than detecting a leak. While detection relies on

detecting the loss of gas in the pipeline, locating a leak requires

detecting the discrepancy in the pressure gradient caused by a

leak. The node flow discrepancies are affected by every factor

that can influence the pressure gradient. Therefore,

uncertainties in any of the following can result in errors in the

RTTM computed node flow discrepancies:

1. Pressure measurements

2. Temperature measurements and thermal properties of

pipeline surroundings

3. Pipeline roughness

4. Unmetered supplies or deliveries

5. Time skew in the data

6. Fluid properties (gas composition)

7. Elevation profile

8. And more…

We therefore assert the following:

Pinpointing a leak to the nearest pressure measurement or the

pipeline subnetwork bounded by pressure measurements is

only possible if the leak size is somewhat larger than the

uncertainties in the flow discrepancies, not just in the vicinity

of the leak, but anywhere in the leak detection section bounded

by flow meters.



Figure 25 – “True Pressure Profiles”: 25 mmscfh Leak on 10 mmscfh Pipeline Flow

Figure 26 - RTTM Computed Pressure Profiles: Leak at Midpoint between Pressure Transmitters with 4 Mile Spacing

Figure 27 - RTTM Computed Pressure Profiles: Leak at Midpoint between Pressure Transmitters with 24 Mile Spacing


Figure 28 – Leak Proximity Signals for Leaks at 0.25, 0.5, 0.75, and 1 Fractional Locations: 25 mmscfh Leak on 0 mmscfh

Initial Flow, 36 Mile Pressure Spacing








Figure 32 – Left and Right Node Flow Discrepancies for 25 mmscfh Leak at 0.25 Fractional Location between PTs spaced 8

Miles Apart: 0 mmscfh Pre-Leak Flow Rate

Figure 33 – Left and Right Node Flow Discrepancies for 25 mmscfh Leak at 0.25 Fractional Location between PTs spaced 8

Miles Apart: 0 mmscfh Pre-Leak Flow Rate


Complications of Real Pipeline Conditions

Unmetered Supply or Delivery Flows

Older gas pipelines often have a significant number and volume

of flows into or out of the pipeline that are not metered in real

time. This poses a particularly difficult problem for pipeline

leak detection as a leak is essentially just an unmetered flow

from the pipeline. The uncertainty in the total unmetered flow

into or out of the pipeline directly affects the obtainable

sensitivity of a leak detection system. Certainly, to the extent

that the unmetered flows are constant or slowly changing, one

may be able to compensate somewhat for them by detecting the

change in Volume Balance from some previous value

considered “normal”. But, this is a poor substitute for accurate

flow measurements. Leak detection best practice would ensure

that all flows into and out of the pipeline are metered.

Dealing with Pipeline Conditions Inconsistent with RTTM Pipeline Representation

When one configures an RTTM, it is difficult to anticipate all

possible contingencies that might occur during pipeline

operations. For example, taking a portion of the pipeline that is

modeled by the RTTM temporarily out-of-service for

maintenance poses difficulties for the RTTM. In order

accommodate these types of operations, RTTM solutions will

need to provide more flexibility to allow pipeline engineers to

take portions of the RTTM leak detection out of service while

the RTTM is running without impacting the leak detection on

other portions of the pipeline.

The authors anticipate that RTTM providers will have to

enhance their products to deal with these types of issues.

The “Art” of Leak Detection

This paper has focused on the “science” of leak detection.

However, an effective RTTM must deal with the following real

world conditions:

1. Data points offline

2. Errors in measurements

3. Uncertain valve statuses

4. Time skew in data

5. Uncertainties fluid properties

6. Uncertainties in ground thermal properties and ground

temperatures

7. “Noisy” pipeline pressures and flows

8. And on and on…

The effectiveness of an RTTM is dependent on the skill at

which the RTTM developer deals with these issues. But, that is

a topic for another paper…

Conclusions

Feasibility of Applying RTTM Technology to Gas Pipelines

RTTM based gas pipeline leak detection can be a very effective

method for detecting and locating large and small leaks on a gas

pipeline system. However, there are challenges that are not

experienced on liquid pipelines. Dealing with some of these

optimally may require R&D on the part of RTTM suppliers.

Importance of Pipeline Instrumentation

Unfortunately, many gas pipelines are not well instrumented for

leak detection. This limits both the leak detection sensitivity

and leak pinpointing capabilities of an RTTM. The RTTM still

provides benefit in this case but not to the extent that is possible.

The paper has demonstrated that RTTM leak detection and

location performance is dependent upon the accuracy, spacing,

and completeness of pipeline instrumentation. Due to the

compressibility of the gas, and the slow rate at which a leak

signal may propagate to distant measurements, the spacing of

pipeline pressures is more critical in gas pipelines than in liquid

pipelines. This is especially true due to the fact that reliable and

rapid rupture detection in a gas pipeline may rely on the RTTM

in ways that a liquid pipeline does not.

We restate here the characteristics of a gas pipeline well

instrumented for leak detection:

1. Every flow into and out of the system is metered and in

SCADA

2. Inline pipeline flow meters subdivide the larger pipeline

into smaller segments that can be independently monitored

for leaks

3. Pressure is available in real time at frequently spaced

intervals.

4. Pressure is available in real time at all pipeline branch

points.

5. Pressure is available in real time on each side of every

active device in the pipeline including valves, compressors,

and regulators

6. The open fraction of every valve that can affect flow paths

in the pipeline is available in real time. Open fraction is

much more useful than simply open, closed, and in-transit

status.

7. Temperature is available in real time at frequently spaced

intervals and for all pipeline supplies and deliveries

8. The gas composition is available in real time for all

supplies unless essentially constant.

9. All real time data is updated by SCADA every few seconds

with high resolution and no deadbands.

10. Good knowledge of ground temperature – preferably

measured.

11. All measurement inputs are accurate, reliable, and have

high resolution


Importance of Attention to Detail in RTTM Modeling

The compressibility inherent in gas pipelines results in large

and persistent transients in gas pipelines. These imposed much

higher demands on modeling fidelity than do liquid pipelines.

The only representation of the pipeline packing rate comes from

the RTTM, and the packing rate can be very large, requiring

that the RTTM be very accurate. Accuracy of the RTTM

requires attention to detail in many areas including:

1. Thermal modeling of the fluid as well as the pipeline

surroundings

2. An accurate equation of state (e.g. AGA8)

3. Appropriate filtering of input data prior to using it as inputs

to the pipeline model

4. Accurate solution of the differential equations describing

the dynamic state of the pipeline. Approximations and

simplifications that may have worked well for liquid

pipelines may not be appropriate in gas pipelines.

References

1. “Computational Pipeline Monitoring for Liquids,” API

1130, American Petroleum Institute

2. The impact of the pipe and ground on pipeline temperature

transients, Nicholas, Ed; Pipeline Simulation Interest

Group, May 2011

3. Accurately Representing Leak Detection Capability and

Determining Risk, Carpenter, Philip S.; Nicholas, Ed;

Henrie, Morgan, Pipeline Simulation Interest Group,

November 2005

4. “Pipeline Variable Uncertainties and their Effects on Leak

Detection,” API Publication 1149, November 1993

5. Bayesian Belief Networks for Pipeline Leak Detection,

Carpenter, P., Nicholas, E. and Henrie, M., Pipeline

Simulation Interest Group, 2006

6. Simulation of Slack Line Flow: A Tutorial, Nicholas, Ed;

Pipeline Simulation Interest Group, 1995

About the Authors

Collectively, the authors have 80 years of experience with

RTTM based leak detection systems.

Ed Nicholas has been designing, implementing, and supporting

real time pipeline modeling applications for 35 years. He

managed simulation development at CRC Bethany, Real Time

Systems, and Scientific Software - Intercomp before branching

END NOTES

i For completeness sake, we note that the statement of

conservation of standard volume is only an approximation to

the statement of conservation of mass. In fact, restating the

concept of conservation of mass as conservation of standard

volume is precisely correct only for a gas that is ideal at

out as Nicholas Simulation Services in 1995. He led the

development of and currently provides primary support for the

leak detection system of the Trans-Alaska Pipeline. His

primary clients have been Koch Industries, PRCI, Sacramento

Municipal Utility District, and Alyeska Pipeline Services

Company. Ed received a BS in physics and mathematics from

Houston Baptist University and a MS in applied physics from

the California Institute of Technology.

Ed Nicholas brings a great deal of experience to the subject of

compressible product leak detection in general and gas pipeline

leak detection specifically. His experience includes:

1. In the early 80’s working alongside the control rooms of

Alberta Gas Ethylene (Red Deer, Alberta) and the Cochin

Pipeline (batched Ethane, Ethylene, Propane, and Butane)

implementing and troubleshooting RTTM based leak

detection systems.

2. Managing the development and performing the installation

of an RTTM model (without leak detection) for the East-

West NGL pipeline in Saudi Arabia.

3. Developed and installed an RTTM based leak detection

system integrated with a rarefaction wave system for Air

Products carbon monoxide pipelines along the Houston

ship channel.

4. He led the development and installation of an RTTM based

leak detection system for Australia’s largest gas pipeline,

the Dampier to Bunbury Pipeline.

5. Developed and supports a leak detection system for the

Fuel Gas Pipeline operated by Alyeska pipeline.

Philip Carpenter is an engineering consultant with over 35 years

of experience in the areas of pipeline hydraulics, pipeline

operation and control, real time systems, statistical analysis, and

numerical simulation of physical processes. His company,

Great Sky River Enterprises, LLC, provides services in these

areas to the oil and gas industry. He has a B.S. in Aerospace

Engineering and an M.S. in Engineering Science, both from the

State University of New York at Buffalo, and is a registered

Professional Engineer in the Commonwealth of Pennsylvania.

Morgan Henrie is the owner of MH Consulting, a firm

providing national and international support to the petroleum

industry. He has been involved in designing, evaluating, and

implementing Supervisory Control and Data Acquisition

systems, telecommunications infrastructures and leak detection

systems for more than 20 years, and has a PhD in Engineering

Management and Systems Science from Old Dominion

University.

standard conditions or whose composition is constant. But,

since all pipeline gases are very close to ideal at standard

conditions (natural gas has a compressibility factor of the order

of 0.998 at standard conditions), restating conservation of mass

as conservation of standard volume is sufficiently accurate for

the purposes of natural gas pipeline leak detection.

Documents

Gas Pipeline Leak Detection - A Tutorial · 2018-04-18 · hydraulics using some (or all) of the real time pipeline measurements as inputs. The RTTM simulation of the pipeline operation