Slide 1

SOFTWARE-BASED PIPELINE LEAK DETECTION*
Presented by: Miguel J. Bagajewicz , James Akingbola**, Elijah Odusina** and David Mannel**
University of Oklahoma School of Chemical, Biological and Material Engineering

(*) This work was done as part of the capstone Chemical Engineering class at the University of Oklahoma
(**) Capstone Undergraduate students

Abstract: Pipeline leak detection has been a focus of numerous research in industry. There are several methods based on expensive hardware. As an alternative, less costly
software based methods have been proposed. These methods make use of the measured flows and pressures to infer through data reconciliation and bias detection
methodologies whether a leak or a bias is present. In this presentation, the Generalized Likelihood Ratio (GLR) method proposed by Narasimhan and Mah (1987) is adapted
to combined leak detection and instrument bias identification. The methodology is entirely implemented within a simulator.

Introduction
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Hardware Method

Pipelines used as bulk carriers of crude oil and natural
gas
Used in water distribution systems
Results of Leakages include:
 Loss of product
 Environmental hazards
 Loss of life

Multi-hop Sensor Wireless Network
Professor Sridhar Radhakrishnan

Solution

Problem: Develop continuous real-time

Multi-hop wireless sensor network with
appropriate sensor fusion technologies.

monitoring of pipelines to determine leak and
other structural damages.

FIBER OPTIC
METHOD

ACOUSTIC EMISSION
METHOD

in both human, property, and environmental
damage.

Limitations
 System costs are usually high because of
the amount of instrumentation.

Current Solution: Low flying aircraft, visual

 High complexity of installation

Weaknesses: Expensive, non-continuous

inspection, and use of pigs for internal
monitoring.

VAPOR SENSING
METHOD

Generalized Likelihood Ratio

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Generalized Likelihood Ratio

Research Issues
• Communication in the presence of
unreliable sensors
• Power aware strategies
• Optimal sensor configuration and data
fusion

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3

Overall Power vs. Simulated Magnitude
(3% Process Variance)

Error vs. Simulated Magnitude
(3% Meter Variance)

Procedure

 This is a statistical method modeled
after the flow conditions in the
pipeline
 A mathematical model that describes
effects of leaks on the flow process
is derived.
 Can detect leaks in pipeline branch,
location in the branch and
magnitude of the leak.
 Can differentiate various types of
gross errors

 Formulate the hypotheses
for gross error detection
without leaks and biases,
Ho and with leaks and
biases, H1
 Use the likelihood ratio test
statistics, λ to test the
hypothesis for gross errors
 Determine the magnitude of
gross errors, b

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Advanced pipeline network used to test the GLR method
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5

Simulation Procedure

Limitations

 Highly dependent on instrument
sensitivity
 Smaller leaks typically takes
longer time to detect
 Does not give magnitude of the
leak

Importance: Failure of gas pipelines will result

monitoring, fail-first, fix-later solutions.

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Software Method
Gradient Intersection Method

Simulation Results

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Simulation Results

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Future Research
 Explore More Complex Networks

The likelihood ratio was tested on a sample pipeline network using PRO II simulation

 Multiple Leak Detection
REFERENCES

Test pipeline network

Test specification

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 Alan S. Willsky, and Harold L. Jones. "A Generalized Likelihood Ratio Approach to the Detection and Estimation of
Jumps in Linear Systems." IEEE (1975): 1-5
 H. Prashanth Reddy. Leak Detection in Gas Pipeline Networks Using Transfer Function Based Dynamic Simulation
Model. Madras, India: Department of Civil Engineering Indian Institute of Technology Madras Chennai, 2006.
 Miguel J. Bagajewicz and Emmanuel Cabrera. "Data Reconciliation in Gas Pipeline Systems." Ind. Eng. Chem. Res 42,
No.22(2003): 1-11.
 Misiunas, Dalius. Failure Monitoring and Asset Condition Assessment in Water Supply Systems. ISGN 91-88934-40-3.
Lund, Sweden: Department of Electrical Engineering and Automation Lund University, 2005.
 Mukherjee Joydeb, Shankar Narasimhan, and "Leak Detection in Networks of Pipeline by the Generalized Likelihood
Ratio Method." Ind. Eng. Chem. Res 35(1996): 1-8.
 Dipl.-Physiker Ralf Tetzner. "Model-Based Pipeline Leak detection and localization." FACHBERICHTE 42(2003):
455-460

PRO II Simulation

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