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 Hardware Method 2 Software Method Gradient Intersection Method Software Method Gradient Intersection Method 3 Generalized Likelihood Ratio 4 5 Overall Power vs. Simulated Magnitude (3% Process Variance) Overall Power vs. Simulated Magnitude (3% Process Variance) 6 Error vs. Simulated Magnitude (3% Meter Variance) Error vs. Simulated Magnitude (3% Meter Variance) 7 Simulation Procedure 8 Simulation Results 9 10 Future Research  Explore More Complex Networks  Multiple Leak Detection REFERENCES  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,  Miguel J. Bagajewicz and Emmanuel Cabrera. "Data Reconciliation in Gas Pipeline Systems." Ind. Eng. Chem. Res 42, No.22(2003):  Misiunas, Dalius. Failure Monitoring and Asset Condition Assessment in Water Supply Systems. ISGN Lund, Sweden: Department of Electrical Engineering and Automation Lund University,  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): Future Research  Explore More Complex Networks  Multiple Leak Detection REFERENCES  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,  Miguel J. Bagajewicz and Emmanuel Cabrera. "Data Reconciliation in Gas Pipeline Systems." Ind. Eng. Chem. Res 42, No.22(2003):  Misiunas, Dalius. Failure Monitoring and Asset Condition Assessment in Water Supply Systems. ISGN Lund, Sweden: Department of Electrical Engineering and Automation Lund University,  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): ACOUSTIC EMISSION METHOD VAPOR SENSING METHOD FIBER OPTIC METHOD Limitations  Highly dependent on instrument sensitivity  Smaller leaks typically takes longer time to detect  Does not give magnitude of the leak (*) This work was done as part of the capstone Chemical Engineering class at the University of Oklahoma (**) Capstone Undergraduate students  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 Procedure  Formulate the hypotheses for gross error detection without leaks and biases, Ho and with leaks and biases, H 1  Use the likelihood ratio test statistics, λ to test the hypothesis for gross errors  Determine the magnitude of gross errors, b Advanced pipeline network used to test the GLR method Multi-hop Sensor Wireless Network Professor Sridhar Radhakrishnan Multi-hop Sensor Wireless Network Professor Sridhar Radhakrishnan 3 Problem: Develop continuous real-time monitoring of pipelines to determine leak and other structural damages. Importance: Failure of gas pipelines will result in both human, property, and environmental damage. Current Solution: Low flying aircraft, visual inspection, and use of pigs for internal monitoring. Weaknesses: Expensive, non-continuous monitoring, fail-first, fix-later solutions. Solution Multi-hop wireless sensor network with appropriate sensor fusion technologies. Research Issues Communication in the presence of unreliable sensors Power aware strategies Optimal sensor configuration and data fusion Limitations  System costs are usually high because of the amount of instrumentation.  High complexity of installation The likelihood ratio was tested on a sample pipeline network using PRO II simulation Test pipeline network Test specification PRO II Simulation 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  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 Introduction  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 1