Leak Detection
Performance
Pipeline Safety Trust Conference
November 17 & 18, 2011
New Orleans, Louisiana
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Why Leak Detection?
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Why Leak Detection? (Contd)
S Minimize the volume of lost fluid
S Reduce risk of fire, explosion or other safety hazards
S Protect the company
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Minimize cleanup costs with an early response to a leak warning
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Protect reputation
S Demonstrable acceptance of responsibility by executing a
proactive leak detection program
S Always increasing regulatory interest in pipeline integrity
programs; including leak detection
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History
S Manual pressure/flow readings and evaluation
S Voice communication between the field and any center of
responsibility that may exist, or with other stations along the
line
S Familiarity with the pipeline behavior by controllers and
technicians was critical
S Right-of-way observation including walk-overs and fly-overs
were among few leak detection options
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Automated Measurements
S Proprietary logging systems and SCADA
S Transmitter and meter technology
S Automation tools and products that acquire accurate
information about hydraulic behavior
S Tools to assist in evaluating the relationships among
measurements
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Leak Detection Performance
S From the former API 1155 (Evaluation Methodology for
Software Based Leak Detection Systems):
S The system correctly indicates that there is no leak
S The system correctly indicates that there is a leak
S The system incorrectly indicates that there is a leak (false
alarm)
S The system incorrectly indicates that there is no leak (failure to
detect)
S Definitions absorbed into API RP-1130 (Computational
Pipeline Monitoring for Liquid Pipelines)
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Performance Metrics
S Sensitivity – Combination of the size of a detectable leak
and the time required to detect it
S Reliability – A measure of the system’s ability to accurately
assess whether a leak exists or not
S Accuracy – The ability of a system to estimate leak
parameters such as leak flow rate, total volume lost, and
leak location
S Robustness – The ability of a system to continue to function
during unusual hydraulic conditions or when data is
compromised
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Meter-Based Leak Detection
S Most widely used method on long haul pipelines
S Requires meters at all entry and exit points
S Achievable sensitivity limited by meter accuracy
S Some methods can be implemented in SCADA; or are
options with SCADA products
S Some products are stand-alone and driven by SCADA data
S Alarm thresholds must tolerate and expect the imbalance in
meter readings as the line packs and unpacks
S Leak evaluation algorithms that correlate actual changes in
linepack with meter imbalance provide the best performance
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API 1149 Performance
Predictions
S Predicts the theoretical best performance possible with
configured uncertainties
S No margin for false alarm prevention
S Temperature uncertainty and related changes in density during
transit is largest influence on performance
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Not uncertainty in measurement, but uncertainty in temperature
profile along the line
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One type of leak detection system can more accurately estimate
the temperature profile than other systems
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Definitions
S Dry Volume – Volume of the pipeline at atmospheric
pressure and a reference temperature
S Linepack – The incremental quantity of fluid in the line in
addition to the dry volume influenced by pressure and fluid
temperature
S Linepack is heavily influenced by temperature’s effect on fluid
density as is pressure, but often to a lesser degree
S Profile – Value of a parameter over the length of the
pipeline segment
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Definitions (cont)
S Uncertainty – Potential error in measurements that must be
expected and accounted for; also the degree to which
something is unknown and must be estimated or assumed
based on somewhat related measurements.
S Real-Time Transient Model (RTTM) – Accurately tracks
fluid temperature/density profiles with consideration of
pressure in order to reduce uncertainty in the linepack
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Meter Quality vs. Performance
Influence of Meter Quality on Performance
4.5
4.16
4.2
4.1
4.1
% Nominal Flow (74 kbph)
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3.5
3
.1, .1 % Meters
.25, .25 % Meters
2.5
.5, .5% Meters
2.26
2.1
2.1
2.0
2
.1, .25 Meters
.1, .5 % Meters
1.68
1.5
1.4
1.43
1.4
1.29
1.2
1.141.08
1.1
1.1
1.1
1.0
1.061.04
1.0
0.9
0.9
0.8 0.9 0.8 0.8
0.8
0.7
0.6
0.7
0.6
0.6 0.5 0.6 0.6
0.5
0.5 0.4
0.4 0.4
0.4 0.4
0.3
1.5
1
0.5
.1, 1% Meters
1.03
1.02
1.01
1.01
1.01
0.7
0.5
0.4
0.3
0.3
0.7
0.5
0.4
0.3
0.2
0.7
0.5
0.4
0.3
0.2
0.7
0.5
0.4
0.3
0.2
0.7
0.5
0.4
0.3
0.1
0
0
10
20
30
40
50
60
70
80
90
Detection Time
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Meter-Based Leak Basics
S Flow/Pressure/Temperature measurements only at segment
end-points
S Metered flow accuracy important for high sensitivity
S Fluid density (molecules/mass per unit volume) varies
significantly with temperature
S Warm fluid is less dense; Cold fluid is more dense
S Temperature/density profile uncertainty is the most limiting
factor in leak detection for some pipelines
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Volumetric Flow Measurement
S Benefits of volumetric (not corrected for temperature and
pressure) flow measurement
S Barrel-to-barrel assessment tolerates differences in density at
injection and delivery points
S Tolerates switching injection sources of different temperatures
S Useful on short lines with small changes in
temperature/density during transmission
S Not useful where temperature/density profiles are significant
due to heat loss
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Net barrel Flow Measurement
S Benefits of net (corrected for temperature and pressure) flow
measurement
S Useful for custody transfer
S Under steady-state conditions, fluid injected at lower density
will balance with delivered fluid at higher density
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Injections at slightly higher volumetric flow balances with
deliveries at lower volumetric flow
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Assumes a stable temperature/density profile
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Net Flow Measurement Issues
S Transient conditions thwart many algorithms involving net
flow measurements
S Line packing and unpacking still result in an apparent net flow
imbalance as with basic volume balance methods
S Net barrel flow measurements aggravate simple balance
algorithms where a difference in density exists at injection
and delivery points except under steady-state stable
conditions
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Natural Flow Imbalance
S Difference in injected fluid density and delivered fluid
density
S Gradual change in fluid density during transit
S Temperature/density profiles are poorly understood by most
simple algorithms
S Profile changes shape with changes in flow rate
S Water crossings and occasionally wet soil increase thermal
conductivity
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Meter-Based Leak Detection
Limitations
S Accounting for changes or disturbances in linepack
S Operational changes causes the line to pack or unpack as a
normal occurrence
S Short-term hydraulic disturbances (transients), including
changes in injection temperatures, and their effect on linepack
must be tolerated or understood in short-term evaluation
algorithms used in rapid assessments
S Over extended periods any effects of transients are diluted;
thus allowing good sensitivity by most meter-based solutions
over long observation intervals
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Key Concepts
S Pressures and temperatures under steady-state conditions provide
current linepack characteristics
S Divergence of meter readings (greater imbalance) should be reflected
in linepack changes as seen in pressures
S Transients disturb this information and can lead to significant shortterm linepack uncertainty
S Over long time intervals any observed variations in linepack become
insignificant compared to the quantity of fluid passing through the
pipeline system
S Long-term sensitivity settings do not require as much tolerance of
linepack uncertainty as do short term leak detection thresholds
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Linepack Estimation
S Fluid density assumed to be an average of injection and
delivery density
S Weighted average
S Custom algorithms for curve fitting
S RTTM thermal models track fluid density along the line
S Change in flow rate alters the temperature/density profile as
a new quiescent state develops
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Profiles
S Temperature
S Occurs when injection and delivery temperatures differ
S Subject to heat transfer characteristics of environment
S Varies with flow rate / transit time
S Pressure
S Varies as batched fluids of different characteristics travel
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Varies as batches of different densities travel over mountains
S Profiles are accurately tracked by Real-Time Transient
Models
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Real-Time Transient Model
S Most sophisticated volume/mass balance tool
S Includes a thermal model to increase the accuracy of linepack
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evaluation, thus allowing a shorter detection time for a
detectible leak
Tracks heat transfer along the line and develops accurate
temperature/density profiles as step changes in flow rates or
injection temperatures occur
Allows thermal model tuning to achieve the best performance
by modeling the hydraulic behavior accurately
Automatic tuning capabilities
Instrument sanity checking
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RTTM Myths
S Software is expensive
S Not significantly more expensive
S Software requires special skills
S Training and full technical support is offered by vendors
S Knowledge of the pipeline physical details is needed
S Software requires ongoing maintenance
S Not needed, once performance is satisfactory, but further
tuning is often performed to continually improve performance
S Required if the pipeline network is changed
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Static Pressure Testing
S Extremely high sensitivity
S Zero (0) flow results in zero (0) uncertainty in flow
measurement
S Works well in a relatively incompressible liquid environment
S Requires pressure control equipment
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Static Pressure Testing (contd)
S Shut in under pressure at near operating pressure
S Monitor pressure decay for a period of time
S Drop pressure by half
S Monitor pressure decay for a period of time
S A consistent pressure decay rate indicates decreasing density
due to heat transfer
S A different pressure decay rate indicates a leak whose rate is
pressure dependent
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Static Pressure Test Trends
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Conclusion
S Simple linepack assessment algorithms have a place where
linepack is stable and linepack variations and uncertainty do
not adversely affect leak assessment
S RTTM technology significantly reduces linepack
uncertainty in transient environments and enables leak
detection approaching the limits imposed by meter accuracy
S Static pressure testing is a useful integrity verification tool
S Matching the proper tool to the pipeline’s operation is
critical
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Training & Response
S A critical component of any pipeline integrity management
program
S Response protocol should be included in training and be
enforced
S Controller can shut down the line on any suspicion of a leak
S Details regarding actions, reporting, etc.
S No internal penalty for reasonable judgment
S A culture focused on pipeline integrity management is
critical
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References
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API 1130 – Computational Pipeline Monitoring for Liquid Pipelines
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API 1149 – Pipeline Variable Uncertainties and Their Effects on Leak
Detectability
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API 1161 – Guidance Document for the Qualification of Pipeline
Personnel
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49 CFR Part 192 - TRANSPORTATION OF NATURAL AND OTHER
GAS BY PIPELINE: MINIMUM FEDERAL SAFETY STANDARDS
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49 CFR Part 195 - TRANSPORTATION OF HAZARDOUS LIQUIDS
BY PIPELINE
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Contact UTSI
UTSI - U.S. Headquarters
1560 West Bay Area Boulevard
Suite 300
Friendswood, Texas USA 77546
Telephone:
+1 281 480 8786
Fax:
+1 281 480 8008
Email:
info@utsi.com
WWW:
http://www.utsi.com
Daniel W. Nagala (dnagala@utsi.com)
President & CEO
November 17 & 18, 2011
UTSI - Europe
Raimundo Fernandez Villaverde 43, 6L
28003 Madrid, Spain
Telephone:
Fax:
Email:
WWW:
+34 (91) 534 07 49
+34 (91) 535 42 57
info@utsi.com
http://www.utsi.com
Catalina Frey (cfrey@utsi.com)
Senior Consultant
Daniel W. Nagala (dnagala@utsi.com)
President & CEO
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