Transcript Methods of Leak Detection

PIPELINE LEAK DETECTION
Eric Penner
Josh Stephens
4/30/09
OVERVIEW
Introduction
Methods of Leak
Detection
Cost Comparison
Introduction
WHERE ARE PIPELINES LOCATED?


Roughly 500,000 miles of
pipeline in US

300,000 miles of gas
pipeline

200,000 miles of oil pipeline
About 1.2 million miles of
pipeline in the world

Russia and Canada are next
two on list with ~250,000
miles and 100,000 miles of
pipeline, respectively
DIFFERENT PIPELINE SYSTEMS
SIGNIFICANT INCIDENTS

Significant incidents meet any of the following
conditions as defined by the PHMSA

Fatality or injury requiring hospitalization

$50,000 or more in total costs, measured in 1984 dollars

Highly volatile liquid releases of 5 bbls or more or other
liquid releases of 50 bbls or more

Any liquid releases resulting in an unintentional fire or
explosion
SIGNIFICANT INCIDENTS 1988-2008
WHAT ARE THE PRIME CAUSES?
Excavation damage is the number one cause
 Most experts regard corrosion as second leading cause,
feeling that a strong portion of those under the “All
Other Causes” heading are corrosion related as well

Methods of Leak Detection
HARDWARE LEAK DETECTION
Pros

Generally good
sensitivity



Cons

Able to detect large and
small leaks quickly
Leak location can be
estimated via
instrumentation
Previous two points help
minimize environmental
and economic impact in
event of leak
High level of
instrumentation


Installation and
maintenance costs can
be relatively high
Complex installations

Considerable amount
of below surface
activity
IN BRIEF: ACOUSTIC EMISSIONS


Method relies on
escaping fluid giving off
a low frequency acoustic
signal
Acoustic sensors placed
around entire length of
pipeline to monitor
interior pipeline noise
Baseline or “acoustic
map” created
 Deviation from baseline
triggers system alarm

IN BRIEF: VAPOR SENSOR METHOD

Vapor sensing tube
placed along entire
length of pipeline



Tube is permeable
to material being
transported
If leak occurs, some
material diffuses
into tube
Test gas is pumped
through and
analyzed for vapors
of pipeline fluid
IN BRIEF: ULTRASONIC FLOW METER

Generates an axial sonic wave in pipe wall


Difference in time for wave to travel upstream and
downstream allows for computation of flow rate
Relies on mass flow balance
FIBER OPTIC SENSING: BASICS


Probes placed along
pipeline every 0.5
meters
Escaping hydrocarbons
change surrounding
temperature



Liquid leaks ↑ T
Gas leaks ↓ T (Joule
Thompson effect)
Scattered light analysis


Raman (intensity
based)
Brillouin (frequency
based)
PERFORMANCE AND COST
Measurement
Performance
Sensitivity
50 ml/min
Leak Size
Magnitude estimated
Leak Location
Within 1 meter
Detection Time
30 seconds to 5 minutes

Cost
 1200 km single pipeline


~$18 million in equipment costs alone


Roughly the distance from Houston to El Paso
Figure does not include installation
Conclusion: fiber optic leak detection requires a
sizeable upfront investment
SOFTWARE LEAK DETECTION
-
Instrumentation is used
to measure internal
parameters of the pipeline
-
What methods are
available?
1.
2.
3.
Balancing Systems
Pressure Analysis
Generalized Likelihood
Ratio
BALANCING SYSTEMS

Basic principle is conservation of mass
dM L
M I (t )  M O (t ) 
0
dt
.


.
Steady State
assumed
Basic line balance does not compensate for changes in line pack
due to pressure, temperature, or product composition
Volume balance is an enhanced, automated technique, which does
account for line pack correction by assessing changes in volume
due to temperature and/or pressure variations using SCADA
(Supervisory Control and Data Acquisition)
MI
MO
WHY PRESSURE MEASUREMENTS?
 Stream
1 and 2 measured
 Discrepancy in flow measurement
Sensor 1
Leak
Sensor 2
Case 1
0.4
0
0
Case 2
0
0.4
0
Case 3
0
0
-0.4
BALANCING SYSTEMS
Example: 1250 m pipeline
Can identify leaks as small as 5% of flow
Flow metering at the end of each pipeline segment
will not identify location of leak
Cannot distinguish leak from bias
Cannot find location of leak
Cost: ~ $200,000






MI
MO
PRESSURE ANALYSIS

How is this implemented?


Changes in flow produce
changes in pressure transients


Pressure indicators segmenting
pipeline
Propagate through the system
until steady-state is reached
SCADA values used to
calculate theoretical hydraulic
profile or baseline
PRESSURE ANALYSIS





Limitations

Not only leaks cause
disturbances in pressure
changes (junctions, nodes,
bends)


Presence of a leak can be
determined from specific
deviation or combinations
of several deviations
Example: 1250 m long
pipeline
Leaks as small as 5% of
nominal liquid flow
Located with an error
smaller than 5 meters
Cost: ~ $200,000
Cannot distinguish a leak
from a bias
GENERALIZED LIKELIHOOD RATIO
Statistical method modeled after flow conditions in
pipeline
 Mathematical model used that describes effects of
leaks and biases on the flow process
 Detects leaks in pipeline branch, location in the
branch, and magnitude of the leak.
 Identifies various types of gross errors

GLR for Gross Error Identification
Process Model
Steady state model without leak
z xv
zis a measurement vector
xis the true value of state variables
v is the vector of random error
Ax  0
A = constraint matrix
Measurement Bias Model
z  x  v  bei
b is the bias of unknown magnitude in
instrument I
e i = is a vector with unity in position i
Process Leak Model
A mass flow leak in process unit (node) j
of unknown magnitude b can be modeled
by;
Ax  bm j  0
the elements of vector m j correspond to
the total mass flow constraint associated
with node j
Procedure for single gross error
r  Az
When there is no gross error;
Er   0
Covr   V  AQA'
S. Narasimhan and R.S.H. Mah. "Generalized Likelihood Ratio Method for Gross Error Identification." AIChe Journal 33,
No.9(1987): 1514-1519.
GLR for Gross Error Identification
If a gross error due to a bias of magnitude b is
present in measurement I, then;
Er   b Aei
If a gross error due to process leak in magnitude
b is present in node j, then;
Er   bmi
When a gross error due to a bias or process leak
is present;
E r   b f
i
where
 Aei
fi 
m j
For a bias in measurement i
For a process leak in node j
let μ be the unknown expected value of r, we can
formulate the hypotheses for gross error
detection as
H0 :   0
H1 :   b f i
Ho: is the null hypothesis that no gross errors
are present and
H1: is the alternative hypothesis that either a
leak or a measurement bias is present.
b and fi are unknown parameters. b can be any
real number and fi will be referred to as a gross
error vectors from the set F
F  Aei , m j :i  1...n, j  1...m
GLR for Gross Error Identification
We will use the likelihood ratio test statistics to
test the hypothesis by:
  sup
 sup
The maximum likelihood estimate

Prr H1
b  f iV
Prr H 0 
 
 
1
exp  0.5 r  b f i V r  b f i
'

1
exp  0.5r V r
bi f i
'

Ti 
Where:
1

 
1
T  2 ln   sup r V r  r  b f i V r  b f i
'
b, f
'
fi
 f V r
1
b
1
i
Substituting b in the test statistics equation
and denoting T by Ti:

The expression on the right hand side is always
positive. The calculation can be simplified by the
calculation by the test statistics, T as:
1
:
d i2
Ci
1
di  f i V r
'
1
Ci  f i V f i
'

This calculation is performed for every vector
fi in set F and the test statistics T is:
j
T  sup Ti
i
GLR
Mechanical Energy balance
Without leak
With leak of magnitude b and
location lb
Liquids
P1  P2  f (G)
Liquids
P1  P2  f (G, b, lb )
Gases
P1  P2  f (G)
Gases
P1  P2  f (G, b, lb )
Miguel J. Bagajewicz and Emmanuel Cabrera. "Data Reconciliation in Gas Pipeline
Systems." Ind. Eng. Chem. Res 42, No.22(2003): 1-11
GLR
Problem formulation
Without Error:
~
With Error:
~
Min (G i  Gi ) * S  ( Pi  Pi ) * S
2
1
Gi
2
1
Pi
~
Gi,in  Gi,out  0
Pi,in  Pi,out  f (G)
1
Gi
i
i
Subject to:
~
Min (G i  Gi ) * S  ( Pi  Pi ) 2 * S Pi1
2
Subject to:
Gi,in  Gi,out  b  0
So:
Pi,in  Pi,out  f (G, b, lb )
GLR IMPLEMENTATION
Leak detection procedure:

Hypothesize leak in every branch and solve data reconciliation problem

Obtain GLR test statistic for each branch objno_leak –objwith_leak_k

Determine the maximum test statistic objno_leak - objwith_leak_k

We compare the max test statistic with the chosen threshold value:
Max{objno_leak – objwith_leak_k}> threshold value: leak is identified and
located in the branch corresponding to the maximum test statistic
NOTE: Assuming only one possible error
SAMPLE PIPELINE NETWORK
SIMULATION PROCEDURE - LEAK IN PIPE 1
Calculator
Leak simulated in Pipe 1
Optimizer
SIMULATION RESULTS- LEAK IN PIPE 1
Leak Simulated
Pipe 1
Location(m)
4000
Magnitude(kg/s)
4.915
Measured Flow
15.482
Measured
Pressure (KPa)
2420.3
Estimated
Magnitude(kg/s)
Estimated
Location(m)
4.640
4048
Pipe
Best Objective
function
1
15.9834
2
18.0199
3
60.4256
4
60.7056
5
21.3695
6
16.8630
7
78.6864
8
81.0650
9
123.2020
SIMULATION PROCEDURE - LEAK IN PIPE 8
Leak simulated in Pipe 8
SIMULATION RESULTS- LEAK IN
Leak Simulated
Pipe 8
Location(m)
450
Magnitude(kg/s)
2.611
Measured Flow
4.946
Measured
Pressure (kPa)
2160.1
Estimated
Magnitude(kg/s)
Estimated
Location(m)
2.609
450
PIPE 8
Pipe
Best Objective
function
1
126.678
2
97.438
3
101.864
4
123.710
5
126.447
6
126.447
7
63.294
8
0.151
9
159.922
GENERALIZED LIKELIHOOD RATIO
 Results

More accurate to do GLR in Pro II as opposed
to Excel

For a system with a single gross error, GLR
can distinguish between a bias and a leak


Procedure more complex for multiple gross errors
Accuracy of the method increases with
increasing magnitude of simulated bias
Cost Comparison
ECONOMIC VALUE
 Which
method is the most economic?
 Cost = L + P + M + F
 Where
L is the value of product lost due to leaks
 P is the value of lost production (ie, that value of
product that would have been shipped if a leak and
shut down of the pipeline had not occurred)
 M is the maintenance and installation cost of
detection equipment
 F is the value of fines levied for leaks

CALCULATING L (PRODUCT LOST DUE TO LEAK)
Average leak size

PHMSA data provided an average leak size
Adjusted average leak size for sensitivity of detection
method
 Detecting smaller leaks reduces average leak size
 Accounted for frequency of leaks being different
 Detecting smaller leaks results in more detected leaks

1600
1400
1200
1000
800
600
400
200
0
Correction Factor for Leak
Frequency
Correction Factor
Adjusted Average Leak Size
Average Leak Size (bbl)

y = -5E-05x4 + 0.009x3 - 0.515x2 + 13.886x +
1147.6
R² = 0.9942
0
20
40
60
Smallest Leak Detected (bbl)
80
1.2
1
0.8
0.6
y = 5E-08x4 - 8E-06x3 + 0.0005x2 0.0119x + 1.0438
R² = 0.9925
0.4
0.2
0
0
20
40
60
Smallest Leak Detected
80
CALCULATING L (PRODUCT LOST DUE TO LEAK)

Price of oil and natural gas
Difficult to accurately predict either
 Oil price varied between $40-$80
 Natural gas price varied between $4-$12


Clean up costs due to leak included

Range from $700 to $5,000 per bbl
CALCULATING P (LOST VALUE PRODUCT TRANSPORTED)
Not the same as leak loss
 Calculated the value lost via shut down of
pipeline to fix leaks



The value of what could have been transported
during that down time
Amount flowing through pipeline: API
Recommended best practices
CALCULATING M (MAINTENANCE) AND F (FINES)
Maintenance assumed to be 5%
of Base Cost for each method
 Fines

EPA fines the costliest
 Cost per bbl estimate

Clean Air Act
 Clean Water Act
 Industry examples


This estimate multiplied by leak
size under each method to
calculate the corresponding fine
METHODOLOGY

GLR compared with Ultrasonic, Volume Balance,
and Pressure Analysis Methods


Pressure analysis methods grouped together since
there is no significant change in base cost or
implementation among them
Excel database created to compare methods
Cost of crew, instrumentation, and different levels of
tuning required were taken into account for each
model
 Various companies were contacted to estimate cost of
different detection schemes

METHODOLOGY

Simulations were run for varying nominal pipe
diameters
2 to 8 inches for gathering/distribution networks
 12 to 24 inches for single pipeline


Multiple scenarios tested for each
Range of values used for price of oil, natural gas, and
for leak clean up
 Pipeline length varied from 0.1 to 10,000 miles
 Time for repair of leak assumed to be the same for all
methods

6” Nominal Diameter: Oil
20” Nominal Diameter: Oil
20” Nominal Diameter: Natural Gas

Example


8000 mi pipeline
~ $1 million in cost
difference between
Ultrasonic and GLR
CONCLUSION

GLR showed to be the most economic for both
single pipelines and gathering/distribution
networks
This held true for oil as well as natural gas
 GLR shows more separation from the other methods
in the case of oil, due to the higher product value


Implementing GLR results in less fines and less
lost production
QUESTIONS
HARDWARE COMPARISON
Method
Power
Acoustic
Emissions
1 false
alarm / year
Not provided
Fiber Optic
Sensing
Reportedly
no false
alarms
Indicates
whether leak is
large, medium,
or small
Vapor
Sensing
Reportedly
no false
alarms
Indicates
whether leak is
large, medium,
or small
0.5% of
monitored
area
Indicated by
difference in
mass flow
measurements
(0.15% nominal
flow smallest)
Known to
be
between
two
ultrasonic
meters
Ultrasonic
Flow Meters
Reportedly
no false
alarms
Size
Estimate of
Leak
Location
+/- 30 m
1m
Smallest
Leak (gas)
Smallest
Leak
(liquid)
Hole 2-10%
of pipeline
dia.
1-3% nominal
flow of
pipeline
15 seconds to
1 minute
30 seconds to
5 minutes
50 ml/min
100 l/hr
Response
Time
1 l/hr
0.15% of nominal flow
2-24 hours
Near real time
CORROSION PREVENTION
Corrosion-related cost to the pipeline industry is
approximately $5.4 to $8.6 billion annually
 Cathodic protection is required on all interstate
pipelines and has been for decades



Technique uses a constant low voltage electrical current
run through the pipeline to counteract corrosion –
corrosion can create a galvanic cell
Pipeline coating is the other common corrosion
prevention
PIGS AND SMART PIGS
•
•
•
Pigs are cylinder shaped plugs
of the same diameter as the pipe
Smart pigs are fitted with
electronic sensors that can help
locate pipeline wall weaknesses
prior to a leak appearing
Both scrape build-up off the
interior wall of the pipeline,
which also helps prevent
corrosion
TRANSIENT FLOW



Advanced fluid mechanics
and hydraulic modeling are
used to simulate pipeline
internal conditions
How is this implemented?
 Pressure and flow
measurements input to
simulation
 Pressure-flow profiles
created
Predicts size and location of
leaks by comparing
measured data to predicted
data

Detectable leaks were greater
than 2% for liquid and 10% for
gas