FORMING THE TOOLSET FOR DEVELOPMENT OF A SYSTEM TO CONTROL QUALITY OF OPERATION OF UNDERGROUND PIPELINES BY OIL AND GAS ENTERPRISES WITH THE USE OF NEURAL NETWORKS
Authors/Creators
- 1. Karpenko Physico-Mechanical Institute of the NAS of Ukraine
- 2. Lviv University of Business and Law
- 3. Ukrainian Academy of Printing
- 4. Zaklad Handlowo-Uslugowy BHP
Description
A set of defining parameters for modeling stages of a surface defect propagation in the outer surface of a metal pipeline taking into consideration fatigue strength has been formed.
For a section of a pipeline with a surface defect, it was proposed to use an algorithm of forecasting polarization potentials using means of neural networks. A procedure of functioning of the testing set was elaborated for estimating efficiency of neural networks. The procedure includes appropriate training methods.
According to the results of analysis of interconnected deformation and corrosion processes, elements of a methodology of formation of information support for forecasting service life of a linear part of underground metal pipelines taking into consideration corrosion fatigue have been developed.
Known results of estimation of service life of underground metal pipelines assumed linear nature of corrosion rate. Relevant information was presented in international and national standards. Recent experimental studies have shown that it is advisable to take into consideration nonlinear nature of corrosion rate in the outer surface of underground metal pipelines (BMP).
A BMP section was inspected with the aid of a polarization potential meter together with a contactless current meter and principles of using neural networks for processing experimental results were formulated. An example of actual BMP was considered and analyzed for metal of a pipe of 17G1S grade steel with a corrosion defect in its outer surface. This analysis has resulted in estimation of metal service life and revealed nonlinearity characterized by magnitude of d=1.136.
A control method and procedures for estimating polarization potentials with the help of neural networks were proposed. They make it possible to describe the process of corrosion defect propagation into the depth of the pipe wall physically sound and mathematically more correct in contrast to the standard procedures.
The information presented is important for improving methods of control of underground metal pipelines operated by oil and gas enterprises, in particular, methods of correct measurement and evaluation of polarization potentials and anode currents in insulation defects taking into consideration nonlinearity of informative parameters
Files
Forming the toolset for development of a system to control quality of operation of underground pipelines by oil and gas enterprises with the use of neural networks.pdf
Files
(1.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:d495040a4e4b5c25e0a6221f3fc55ba0
|
1.1 MB | Preview Download |
Additional details
References
- Lozovan, V., Dzhala, R., Skrynkovskyy, R., Yuzevych, V. (2019). Detection of specific features in the functioning of a system for the anti-corrosion protection of underground pipelines at oil and gas enterprises using neural networks. Eastern-European Journal of Enterprise Technologies, 1 (5 (97)), 20–27. doi: https://doi.org/10.15587/1729-4061.2019.154999
- Wu, Y-Y., Lambert, P., Mangat, P., O'Flaherty, F. J. (2011). Analysis of Stray Current Induced by Cathodic Protection on Steel- Framed Masonry Structures. The Open Corrosion Journal, 4 (1), 34–39. doi: https://doi.org/10.2174/1876503301104010034
- Farzaneh, A., Jaber, N., Jamshid, M. (2015). An Electrochemical Measurement for Evaluating the Cathodic Disbondment of Buried Pipeline Coatings under Cathodic Protection. Iranian Journal of Chemistry and Chemical Engineering, 34 (2), 83–91. Available at: http://www.ijcce.ac.ir/article_14100.html
- Merzah, A. S., Ateeq, A. A., Mustafa, A. A. (2017). An Experimental Investigation of Impressed Current Cathodic Protection system (ICCP) In Basra Soil. International Journal of Scientific & Engineering Research, 8 (7), 1311–1314. Available at: https://www.ijser.org/researchpaper/An-Experimental-Investigation-of-Impressed-Current-Cathodic-Protection-system-ICCP-In-Basra-Soil.pdf
- Din, M. M., Ithnin, N., Zain, A. M., Noor, N. M., Siraj, M. M., Rasol, R. M. (2015). An artificial neural network modeling for pipeline corrosion growth prediction. ARPN Journal of Engineering and Applied Sciences, 10 (2), 512–519. Available at: http://www.arpnjournals.com/jeas/research_papers/rp_2015/jeas_0215_1484.pdf
- Chen, Y., Wang, Z., Wang, X., Song, X., Xu, C. (2018). Cathodic Protection of X100 Pipeline Steel in Simulated Soil Solution. International Journal of Electrochemical Science, 13, 9642–9653. doi: https://doi.org/10.20964/2018.10.23
- Yuzevych, V. M., Dzhala, R. M., Koman, B. P. (2018). Analysis of Metal Corrosion under Conditions of Mechanical Impacts and Aggressive Environments. METALLOFIZIKA I NOVEISHIE TEKHNOLOGII, 39 (12), 1655–1667. doi: https://doi.org/10.15407/mfint.39.12.1655
- Yuzevych, V., Klyuvak, O., Skrynkovskyy, R. (2016). Diagnostics of the system of interaction between the government and business in terms of public e-procurement. Economic Annals-ХХI, 160 (7-8), 39–44. doi: https://doi.org/10.21003/ea.v160-08
- Skrynkovskyi, R. (2008). Investment attractiveness evaluation technique for machine-building enterprises. Actual Problems of Economics, 7, 228–240. Available at: http://www.scopus.com/inward/record.url?eid=2-s2.0-77952681437&partnerID=MN8TOARS
- Lozovan, V., Yuzevych, V. (2017). Neural network as a mean for metal constructions performance enhancement by accounting the interfacial layers. Measuring equipment and metrology, 78, 48–54. Available at: http://science.lpnu.ua/istcmtm/all-volumes-and-issues/volume-78-2017/neural-networks-means-improving-metrological
- Yuzevych, V., Skrynkovskyy, R., Koman, B. (2018). Intelligent Analysis of Data Systems for Defects in Underground Gas Pipeline. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp.2018.8478560
- Mykyychuk, M., Yuzevych, L. (2017). Quality control of underground pipelines in view of corrosion fatigue, durability and regulatory documents. Measuring equipment and metrology, 78, 101–107. Available at: http://science.lpnu.ua/istcmtm/all-volumes-and-issues/volume-78-2017/control-underground-gas-pipelines-quality-taking
- Chonghua, Y., Minggao, Y. (1980). A calculation of the threshold stress intensity range for fatigue crack propagation in metals. Fatigue & Fracture of Engineering Materials and Structures, 3 (2), 189–192. doi: https://doi.org/10.1111/j.1460-2695.1980.tb01113.x
- Hinton, G. E., Osindero, S., The, Y.-W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18 (7), 1527–1554. doi: https://doi.org/10.1162/neco.2006.18.7.1527
- Hornik, K., Stinchcombe, M., White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2 (5), 359–366. doi: https://doi.org/10.1016/0893-6080(89)90020-8
- Saifullin, E. R., Izmailova, E. V., Ziganshin, S. G. (2017). Methods of Leak Search from Pipeline for Acoustic Signal Analysis. Indian Journal of Science and Technology, 10 (1). doi: https://doi.org/10.17485/ijst/2017/v10i1/109953
- Sanchez-Amaya, J. M., Cottis, R. A., Botana, F. J. (2005). Shot noise and statistical parameters for the estimation of corrosion mechanisms. Corrosion Science, 47 (12), 3280–3299. doi: https://doi.org/10.1016/j.corsci.2005.05.047
- Shi, Y., Zhang, C., Li, R., Cai, M., Jia, G. (2015). Theory and Application of Magnetic Flux Leakage Pipeline Detection. Sensors, 15 (12), 31036–31055. doi: https://doi.org/10.3390/s151229845
- Mitchell, M. R., Link, R. E., Jiang, Q. (2010). Study of Underground Oil-Gas Pipeline Corrosion Pits Estimation Based on MFL Inspection Method. Journal of Testing and Evaluation, 38 (2), 250–253. doi: https://doi.org/10.1520/jte102467
- Parkins, R. N. (1980). Predictive approaches to stress corrosion cracking failure. Corrosion Science, 20 (2), 147–166. doi: https://doi.org/10.1016/0010-938x(80)90128-6
- Parkins, R. N. (1989). The application of stress corrosion crack growth kinetics to predicting lifetimes of structures. Corrosion Science, 29 (8), 1019–1038. doi: https://doi.org/10.1016/0010-938x(89)90091-7
- Klapper, H. S., Goellner, J., Heyn, A. (2010). The influence of the cathodic process on the interpretation of electrochemical noise signals arising from pitting corrosion of stainless steels. Corrosion Science, 52 (4), 1362–1372. doi: https://doi.org/10.1016/j.corsci.2009.12.021
- Colorado-Garrido, D., Ortega-Toledo, D. M., Hernández, J. A., González-Rodríguez, J. G., Uruchurtu, J. (2008). Neural networks for Nyquist plots prediction during corrosion inhibition of a pipeline steel. Journal of Solid State Electrochemistry, 13 (11), 1715–1722. doi: https://doi.org/10.1007/s10008-008-0728-7
- Kenny, E. D., Paredes, R. S. C., de Lacerda, L. A., Sica, Y. C., de Souza, G. P., Lázaris, J. (2009). Artificial neural network corrosion modeling for metals in an equatorial climate. Corrosion Science, 51 (10), 2266–2278. doi: https://doi.org/10.1016/j.corsci.2009.06.004
- Reddy, N. S. (2014). Neural Networks Model for Predicting Corrosion Depth in Steels. Indian Journal of Advances in Chemical Science, 2 (3), 204–207. Available at: https://www.ijacskros.com/artcles/IJACS-M98.pdf
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. doi: https://doi.org/10.1016/j.neunet.2014.09.003
- Schmidhuber, J. (1997). Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability. Neural Networks, 10 (5), 857–873. doi: https://doi.org/10.1016/s0893-6080(96)00127-x
- Endrullat, C., Glökler, J., Franke, P., Frohme, M. (2016). Standardization and quality management in next-generation sequencing. Applied & Translational Genomics, 10, 2–9. doi: https://doi.org/10.1016/j.atg.2016.06.001