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Publisher: Elsevier
Languages: English
Types: Article
A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.\ud \ud A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.
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    • 1. U.S. Energy Information Administration. International Energy Statistics. 2013.
    • 2. Chan CW. An expert decision support system for monitoring and diagnosis of petroleum production and separation processes. Expert Syst Appl. 2005; 29: 131-43.
    • 3. Roverso D. Plant diagnostics by transient classification: The ALADDIN approach. International Journal of Intelligent Systems. 2002; 17: 767-90.
    • 4. Omana M and Taylor JH. Fault Detection and Isolation Using the Generalized Parity Vector Technique in the Absence of an a Priori Mathematical Model. Control Applications, 2007 CCA 2007 IEEE International Conference on. 2007, p. 970-5.
    • 5. Taylor JH and Omana M. Fault Detection, Isolation and Accommodation Using the Generalized Parity Vector Technique. In: Chung MJ and Misra P, (eds.). Proceedings of the 17th IFAC World Congress, 2008. COEX, South Korea 2008, p. 1914-21.
    • 6. Gao Q, Han M, Hu S-l and Dong H-j. Design of Fault Diagnosis System of FPSO Production Process Based on MSPCA. Information Assurance and Security, 2009 IAS '09 Fifth International Conference on. 2009, p. 729-33.
    • 7. Dias AC, Bhaya A and Kaszkurewicz E. Fault Diagnosis in an Oil Production Plant Prototype Using a Diagnostic Model Processor. American Control Conference, 1993. 1993, p. 107-11.
    • 8. Afonso PAFNA, Ferreira JML and Castro JAAM. Sensor Fault Detection and Identification in a Pilot Plant Under Process Control. Chemical Engineering Research and Design. 1998; 76: 490-8.
    • 9. Kinnaert M, Vrančić D, Denolin E, Juričić Đ and Petrovčić J. Modelbased fault detection and isolation for a gas-liquid separation unit. Control Engineering Practice. 2000; 8: 1273-83.
    • 10. Al-Hajri EM and Rossiter JA. A unified frame work for oil producing stations using Petri nets. Control 2010, UKACC International Conference on. 2010, p. 1-8.
    • 11. Arnold K and Stewart M. Chapter 5 - Three-Phase Oil and Water Separation. In: Stewart KA, (ed.). Surface Production Operations (Third Edition). Burlington: Gulf Professional Publishing, 2008, p. 244-315.
    • 12. Svrcek WY, Mahoney DP and Young BR. A Real-Time Approach to Process Control. John Wiley \\& Sons, 2006.
    • 13. Jensen FV and Nielsen TD. Bayesian Networks and Decision Graphs. Second Edition ed.: Springer, 2007.
    • 14. Koller D and Pfeffer A. Object-oriented Bayesian networks. Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 1997, p. 302-13.
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