Key Technologies of Sucker Rod Pump Card Diagnosis Based on BP Neural Network

Xiao Dong Wu; Rui Dong Zhao; Zhang Hao; Tao Zhen; Su Lei

doi:10.4028/www.scientific.net/AMR.201-203.433

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 201-203Key Technologies of Sucker Rod Pump Card Diagnosis...

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.

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Advanced Manufacturing Systems, ICMSE 2011

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Periodical:

Advanced Materials Research (Volumes 201-203)

Pages:

433-437

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.201-203.433

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Online since:

February 2011

Authors:

Xiao Dong Wu, Rui Dong Zhao, Zhang Hao, Tao Zhen, Su Lei

Keywords:

BP Neural Network (BPNN), Diagnosis, Dynamometer Card, Oil Production, Sucker Rod Pump, Training Strategy

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