English

Vol. 9 6, 2019 p 633-639

Pages

Article name, authors, abstract and keyword

633-639

Stochastic model of material balance for leak detection in oil pipelines

Anton M. Chionov a, Artur A. Amerkhanov b, Andrey V. Kudritsky a

a Pipeline Transport Institute, LLC (Transneft R&D, LLC), 47a, Sevastopolsky prospect, Moscow, 117186, Russian Federation

b Transneft, 4, bldg 2, Presnenskaya Embankment, Moscow, 123112, Russian Federation

DOI: 10.28999/2541-9595-2019-9-6-633-639

Abstract: The paper discusses the basic algorithm of parametric systems for leakage detection the material balance method. The main attention has been paid to the issues of its practical use: justifying decision making criteria, eliminating false triggering, evaluating the leakage detection system sensitivity. The material balance method is an algorithm of sequential oil imbalance analysis to detect imbalance deviations from zero. The mathematical formulation of the problem is to find the moment of change in probability characteristics of the random process under investigation. In this case, such a characteristic is the mathematical expectation of oil imbalance. The developed stochastic leakage detection model is based on the consideration of measured oil unbalance in a pipeline section as a stationary ergodic random process. We considered the problem of imbalance of such random process and suggested a way to solve the same from an engineering point of view. We developed a methodology for evaluating the leakage detection algorithm sensitivity depending on the technological site equipment with flow measuring instruments.

Keywords: random sequence imbalance, leakage detection, decision making algorithm, oil pipeline, mode control system, equipment adequacy evaluation.

For citation:
Chionov A. M., Amerkhanov A. A., Kudritsky A. V. Stochastic model of material balance for leak detection in oil pipelines. Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktovScience & Technologies: Oil and Oil Products Pipeline Transportation. 2019;9(6):633639.

References:
[1] Shestakov R. A. On methods of detecting leaks and illegal tapping of trunk pipelines. Proceedings of Gubkin Russian State University of Oil and Gas. 2014(3):8594. (In Russ.)
[2] Golyanov . . Analysis of oil pipeline leak detection methods. Transport and Storage of Oil Products. 2002(10):514. (In Russ.)
[3] Sunagatullin R. Z., Korshunov S. A., Datsov Y. V. Concerning technical and methodological support of leakage detection systems at facilities. Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktovScience & Technologies: Oil and Oil Products Pipeline Transportation. 2017;7(5):4250. (In Russ.)
[4] Landau L. D., Lifshits E. M. Course of Theoretical Physics: 10 volume series. Vol. 6. Hydrodynamics. Moscow: Nauka Publ.; 1986. 736 p. (In Russ.)
[5] Oil pipeline transport. Under general editorship of S. . Vaynshtok. oscow: Nedra-Businesscenter Publ.; 2004. Vol. 2. 621 p. (In Russ.)
[6] Mastobaev B. N. Oil and oil products pipeline transport. Y. V. Lisin, editor. Handbook: in 2 vol. Vol. 1. Moscow: Nedra Publ.; 2017. 494 p. (In Russ.)
[7] Lurie M. V. Improvement of oil and oil products transport safety through introduction of continuous monitoring of liquid weight in pipeline sections. Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktovScience & Technologies: Oil and Oil Products Pipeline Transportation. 2017;7(5):6269. (In Russ.)
[8] Venttsel E. S. Probability theory. Moscow: Nauka Publ.; 1969. 576 p. (In Russ.)
[9] Brodsky B. E., Darkhovsky B. S. Problems and methods of probabilistic diagnostics. Automation and Remote Control. 1999(8):350. (In Russ.)
[10] Sukharev M. G., Kosova K. O., Titov B. A., Chudina M. Y. Stochastic leaks identification model. Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktovScience & Technologies: Oil and Oil Products Pipeline Transportation. 2015(4):8084. (In Russ.)
[11] Shewart W. The application of statistics as an aid in maintaining quality of a manufactured product. Journal of the American Statistical Association. 1925;20(152):546548.
[12] Vasiltchenko S. G. Instant of disorder of random sequence detections algorithm. Fundamentalnaya i prikladnaya matematika (Fundamental and Applied Mathematics). 2002;8(3):655665. (In Russ.)
[13] Darkhovskii B. S. Retrospective detection of the changepoint in some models of regression type. Theory of Probability and its Applications. 1995;40(4):748753. (In Russ.)