Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis. 1. Introduction The sucker-rod pump system is the most widely used form of artificial lift for the onshore oil well production [1–3]. Approximately, 80% of the oil wells in the world, 90% of those in China, are being produced by the sucker-rod pumps [4, 5]. The maintenance and optimization of a sucker-rod pump system is a costly and time-consuming operation. The indicator card is the relation curve between the load and the displacement of a sucker-rod pump in an intact suck cycle, in which -axis represents displacement and -axis represent load [6]. The indicator card is helpful to analyze the down-hole working condition of the sucker-rod pump wells [7], which can judge the operation condition of the sucker-rod pump well and provide reliable proof of high efficiency, reasonable exploitation for the oil well production. While the system is operating, the card can indicate such shape that might be a normal operation or a fault situation. According to different kinds of real-time indicator card data, the pattern recognition and fault diagnosis techniques are used to identify some different curve shapes, locate which kind of abnormal situation is, and interpret why the fault occurs [8]. Therefore, the correct and quick identification of the sucker-rod pump indicator card is essential to the fault diagnosis of down-hole working condition. The automatic fault diagnosis of sucker-rod pump working condition is a visual interpretation process [9]. Nowadays, the traditional methods of interpretation are not suitable for the automatic fault diagnosis of the down-hole conditions. And several signal processing methods, such as
References
[1]
G. P. Schirmer, J. C. P. Toutain, and J. C. Gay, “Use of advanced pattern recognition and knowledge-based system in analyzing dynamometer cards,” SPE Computer Application, vol. 3, no. 6, pp. 21–24, 1991.
[2]
L. Alegre and C. Morooka, “Intelligent diagnosis of rod pumping problems,” in Proceedings of the 68th Annual Technical Conference, vol. 3, pp. 97–108, SPE, 1993.
[3]
G. Han and X. Wu, “Application of dynamometer card identification in diagnosis of working-condition for suck rod pump,” Oil Drilling & Production Technology, vol. 25, no. 5, pp. 70–74, 2003.
[4]
H. Tripp, “A review: analyzing beam-pumped wells,” Journal of Petroleum Technology, vol. 41, no. 5, pp. 457–458, 1989.
[5]
G. Nazi and J. Lea, “Application of artificial neural network to pump card diagnosis,” SPE Computer Application, vol. 6, no. 6, pp. 9–14, 1994.
[6]
M. J. Gao, “Portable intelligent instrument for pumping data collection,” Journal of Transducer Technology, vol. 24, no. 9, pp. 58–62, 2005.
[7]
F. Barreto and M. Tygel, “Automatic down-hole card generation and classification,” in Proceedings of the SPE Annual Technical Conference, pp. 311–318, 1996.
[8]
Z. Pan and J. Ge, “An adaptive neural network for identification of dynamometer card,” Acta Petrolei Sinica, vol. 17, no. 3, pp. 104–109, 1996.
[9]
R. Dickinson and J. Jennings, “Use of pattern-recognition techniques in analyzing down-hole dynamometer cards,” SPE Production Engineering, vol. 5, no. 2, pp. 187–192, 1990.
[10]
D. Baodong and L. Mingshan, “Analyzing dynamometer card of well pumping by using artificial nerve network method,” Well Testing, vol. 7, no. 1, pp. 27–29, 1998.
[11]
W. Shi and J. Dai, “FSVM based recognition approach for down-hole pump dynamometer cards,” Oil Field Equipment, vol. 33, no. 4, pp. 46–48, 2004.
[12]
W. Shoujue, “Bionic (topological) pattern recognition-a new model of pattern recognition theory and its applications,” Acta Electronica Sinica, vol. 30, no. 10, pp. 1417–1420, 2002.
[13]
S. C. Yuan, “Simple distinguish way for dynamometer card,” Well Testing, vol. 14, no. 1, pp. 49–50, 2005.
[14]
B. D. Du and M. S. Li, “Analyzing dynamometer card of well pumping by using ANN method,” Well Testing, vol. 1, no. 7, pp. 27–29, 1998.
[15]
S. H. Yang, “Applications of difference curve methods in diagnosis of indicator diagram,” Petroleum Drilling Techniques, vol. 30, no. 2, pp. 63–64, 2002.
[16]
A. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, USA, 1989.
[17]
H. Kauppien and T. Sepanen, “An experiment comparison of auto regressive and Flourier-based descriptors in 2D shape classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 201–207, 1995.
[18]
R. C. Gonzalez, Digital Image Processing, Electronic Industrial Press, 2005.
[19]
W. Wu and G. D. Chen, “Fault diagnosis system for pump work indicating diagram based on neural network and gray-level matrix,” Journal of Xi'an Shiyou University, vol. 22, no. 3, pp. 119–121, 2007.
