PIPELINE LEAK DETECTION

Eric Penner

Josh Stephens

4/30/09

OVERVIEW

IntroductionMethods 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

Generally good sensitivity 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

Pros Cons

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)

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

Roughly the distance from Houston to El Paso ~$18 million in equipment costs alone

Figure does not include installation Conclusion: fiber optic leak detection

requires a sizeable upfront investment

PERFORMANCE AND COST

SOFTWARE LEAK DETECTION

- Instrumentation is used to measure internal parameters of the pipeline

- What methods are available?

1. Balancing Systems2. Pressure Analysis3. Generalized

Likelihood Ratio

BALANCING SYSTEMS Basic principle is conservation of mass

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)

. .

( ) ( ) 0LI O

dMM t M t

dt

MI MO

Steady State assumed

Stream 1 and 2 measured Discrepancy in flow measurement

WHY PRESSURE MEASUREMENTS?

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?

Pressure indicators segmenting pipeline

Changes in flow produce changes in pressure transients 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 IdentificationProcess ModelSteady state model without leak

is a measurement vector is the true value of state variables is the vector of random error

= constraint matrix

Measurement Bias Model

b is the bias of unknown magnitude in instrument I = is a vector with unity in position i

vxz z

iebvxz

ie

0AxA

Azr

0rE

'AQAVrCov

xv

0 jmbAx

jm

S. Narasimhan and R.S.H. Mah. "Generalized Likelihood Ratio Method for Gross Error Identification." AIChe Journal 33, No.9(1987): 1514-1519.

Process Leak ModelA mass flow leak in process unit (node) j of unknown magnitude b can be modeled by;

the elements of vector correspond to the total mass flow constraint associated with node j

Procedure for single gross error

When there is no gross error;

GLR for Gross Error IdentificationIf a gross error due to a bias of magnitude b is present in measurement I, then;

If a gross error due to process leak in magnitude b is present in node j, then;

When a gross error due to a bias or process leak is present;

let μ be the unknown expected value of r, we can formulate the hypotheses for gross error detection as

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

iAebrE

imbrE

j

i

i

i

m

Aef

where

fbrE

For a bias in measurement i

For a process leak in node j

ifbH

H

:

0:

1

0

mjnimAeF ji ...1,...1:,

GLR for Gross Error IdentificationWe will use the likelihood ratio test statistics to test the hypothesis by:

The expression on the right hand side is always positive. The calculation can be simplified by the calculation by the test statistics, T as:

rVr

fbrVfbr

Hr

Hr

ii

fbii

1'

1'

0

1

5.0exp

5.0expsup

Pr

Prsup

ii

fbfbrVfbrrVrT

j

1'1'

,supln2

b

rVffVfbiii

111

iii

ii

i

ii

fVfC

rVfd

CdT

1'

1'

2

iiTT sup

The maximum likelihood estimate :

Substituting in the test statistics equation and denoting T by Ti:

Where:

This calculation is performed for every vector fi in set F and the test statistics T is:

b

GLRMechanical Energy balance

Without leak

Liquids

Gases

With leak of magnitude b and location lb

Liquids

Gases

)(21 GfPP ),,(21 blbGfPP

Miguel J. Bagajewicz and Emmanuel Cabrera. "Data Reconciliation in Gas Pipeline Systems." Ind. Eng. Chem. Res 42, No.22(2003): 1-11

)(21 GfPP ),,(21 blbGfPP

GLR

Problem formulation

Without Error:

Subject to:

With Error:

Subject to:

So:0,, outiini GG

12~

12~

*)(*)( ii PiiGii

i SPPSGGMin

)(,, GfPP outiini

0,, bGG outiini

),,(,, boutiini lbGfPP

12~

12~

*)(*)( ii PiiGii

i SPPSGGMin

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) 4.640

Estimated Location(m) 4048

PipeBest

Objective function

1 15.9834

2 18.0199

3 60.4256

4 60.7056

5 21.3695

6 16.8630

7 78.68648 81.06509 123.2020

SIMULATION PROCEDURE - LEAK IN PIPE 8

Leak simulated in Pipe 8

SIMULATION RESULTS- LEAK IN PIPE 8

Leak Simulated

Pipe 8  

Location(m) 450

Magnitude(kg/s) 2.611

Measured Flow 4.946

Measured Pressure (kPa) 2160.1

PipeBest

Objective function

1 126.6782 97.4383 101.8644 123.7105 126.4476 126.4477 63.2948 0.1519 159.922

Estimated Magnitude(kg/s) 2.609

Estimated Location(m) 450

GENERALIZED LIKELIHOOD RATIO

ResultsMore accurate to do GLR in Pro II as

opposed to ExcelFor 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 leaksP 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

0 10 20 30 40 50 60 70 800

0.2

0.4

0.6

0.8

1

1.2

f(x) = 0.000000046484 x⁴ − 0.00000799418 x³ + 0.000454454 x² − 0.0118664 x + 1.04382R² = 0.992481733299527

Correction Factor for Leak Frequency

Smallest Leak Detected

Corr

ecti

on F

acto

r

0 10 20 30 40 50 60 70 800

200400600800

1000120014001600

f(x) = − 0.0000511551 x⁴ + 0.00903263 x³ − 0.514984 x² + 13.8861 x + 1147.64R² = 0.994198831330397

Adjusted Average Leak Size

Smallest Leak Detected (bbl)

Aver

age

Leak

Size

(bbl

)

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 COMPARISONMethod Power Size

Estimate of Leak

Location Smallest Leak (gas)

Smallest Leak

(liquid)

Response Time

Acoustic Emissions

1 false alarm / year Not provided +/- 30 m

Hole 2-10% of pipeline

dia.

1-3% nominal flow of pipeline

15 seconds to 1 minute

Fiber Optic Sensing

Reportedly no false alarms

Indicates whether leak is large, medium,

or small1 m 50 ml/min

30 seconds to 5 minutes

Vapor Sensing

Reportedly no false alarms

Indicates whether leak is large, medium,

or small

0.5% of monitored

area 100 l/hr 1 l/hr 2-24 hours

Ultrasonic Flow Meters

Reportedly no false alarms

Indicated by difference in

mass flow measurements (0.15% nominal flow smallest)

Known to be

between two

ultrasonic meters

0.15% of nominal flow 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