Paper Title:
Key Technologies of Sucker Rod Pump Card Diagnosis Based on BP Neural Network
  Abstract

Sucker rod pumping is a dominated artificial lift method for oil production engineering. In the production process, diagnosing the condition of pump using dynamometer card is vitally significant to monitor and manage the pumping system. With the ability to reflect arbitrary non-linear mappings, the BP neural network can be used in pattern recognition of the pump dynamometer card. In this paper, some key technologies of establishing reasonable neural network are introduced. The number of neurons in input layer depends on the selection of characteristic value. The number of neurons in hidden layer can be obtained by some models, optimum value will be chosen out. The number of neurons in output layer depends on the recognized behavior of pump. After the construction of neural network, the more effective and practical BP neural network will be obtained by suitable samples and appropriate training strategies.

  Info
Periodical
Advanced Materials Research (Volumes 201-203)
Edited by
Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang
Pages
433-437
DOI
10.4028/www.scientific.net/AMR.201-203.433
Citation
X. D. Wu, R. D. Zhao, Z. Hao, T. Zhen, S. Lei, "Key Technologies of Sucker Rod Pump Card Diagnosis Based on BP Neural Network", Advanced Materials Research, Vols. 201-203, pp. 433-437, 2011
Online since
February 2011
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Price
$32.00
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