The oil extraction process requires continuous monitoring of the oil well equipment operation. One of the most effective methods of the on-line control of sucker-rod pumps operation is obtaining information from the force sensor in the polished rod or the current sensor of the pump jack driving motor. In many cases, timely troubleshooting and preventive repair allow saving large costs. Therefore, studies in the area of developing diagnostic systems and, on their basis, creating automated control systems for sucker-rod pumping units (SRPU) are of topical value.
The paper discusses the neural-network-based approach to solving the tasks of forecasting the technical status of jack pumps. The modified Hopfield network (Hamming network) was used as a neural network, for which an SRPU status identification algorithm was devised. Due to it, the identification process outputs not the sample curve itself, but only its number, which results in the faster neural network and smaller computing resources and memory required.
For testing the performance of the proposed identification algorithm, a laboratory bench simulating the operating SRPU status diagnostic system was created. The obtained experimental data show that the Hamming-network-based identification system can perform real-time diagnosing of the current status of the downhole equipment with the minimum error.
- А.F.Shageev, А.М.Timusheva, L.N.Shageeva and А.S.Grishkin, "Automated monitoring of the oil well treatment - the first stage of intelligent control systems", Neftyanoye khozyaistvo, no. 11, pp.48–49, Moscow, Russia, 2000. (Russian)
- А.S. Galeev, R.I. Аrslanov, P.P. Yermilov, and I.А. Kuzmin, "Control of technical condition oil-well pumping unit under periodic operation conditions", http://ogbus.ru/authors/GaleevAS/GaleevAS_2.pdf. (Russian)
- М.I. Khakimyanov and S.V. Svatlakova "Optimal methods for encoding dynamogamms of deep-well pumping units", іn Electrotechnology, electric drive and electrical equipment of enterprises, pp. 146-150, Ufa, Russia: UGNTU, 2005. (Russian)
- P. Lionel Evina Ekombo, Noureddine Ennahnahi and Mohammed Oumsis, "Application of affine invariant Fourier descriptor to shape-based image retrieval", International Journal of Computer Science and Network Security (IJCSNS), vol.9, no.7, pp. 240 – 247, 2009.
- S. Mallat A wavelet tour of signal processing. Мoscow, Russia: Mir, 2005. (Russian)
- Т.А. Aliev, and О.К. Nusratov "The methods and diagnostic tools deep pumping oil well equipment", Neftyanoye khozyaistvo, no. 9, pp. 78–80, Moscow, Russia, 1998. (Russian)
- А.М.Zyuzev, and А.V. Kostylev "A neural-network-based system of the sucker-rod oil pumping unit diagnostics", in Proc. 2nd Russian Scientific Conference "Design of engineering and scientific applications in the MATLAB", pp.1273-1287, Moscow, Russia, May 25-26, 2004. (Russian)
- A.S.Andreishyn, A.V.Malyar, B.S. Kaluzhnyy, and S.M.Leshchuk "Neural network selection for detecting the state of an oil well", Problemy avtomatizirovannoho elektroprivoda. Teoriya i praktika, no.36, pp. 495-496, Kharkiv, Ukraine: NTU KhPI, 2013. (Ukrainian)
- V.S.Medvedev, and V.G. Potyomkin, Neural networks. MATLAB 6. Мoscow, Russia: Dialog-MIFI, 2002. (Russian)
- J.J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities", in Proceedings of National Academy of Sciences USA, vol. 79, no. 8, pp. 2554–2558, 1982. https://doi.org/10.1073/pnas.79.8.2554
- P. Wasserman, Neural Computing. Van Nostrand Reinhold, New York, 1989.
- "STM32 32-bit ARM Cortex MCUs", http://www.st.com/en/microcontrollers/stm32-32-bit-arm-cortex-mcus.html?sc=stm32.