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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Dynamometer card generation for pumping units based on CNN and electrical parameters.

Chunhua Yuan1, Wendong Wu1, Xiangyu Li2

  • 1School of Automation and Electrical Engineering, Shenyang Ligong University, Shengyang, 110159, Liaoning, China.

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|August 12, 2024
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Summary
This summary is machine-generated.

This study introduces a novel method for monitoring oil well pumping units using motor electrical data and a convolutional neural network (CNN). This approach enhances well failure diagnosis by improving the accuracy and reliability of dynamometer card data.

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Accurate dynamometer card data is crucial for diagnosing oil well failures.
  • Traditional load sensors on pumping units are prone to damage and errors in harsh oil field environments.
  • Existing methods lack reliability due to environmental challenges.

Purpose of the Study:

  • To develop a robust and accurate method for acquiring pumping unit data.
  • To overcome the limitations of traditional sensor-based dynamometer measurements.
  • To improve the reliability of well failure diagnosis in oil field production.

Main Methods:

  • A mixed model combining AC motor electrical parameters with a Convolutional Neural Network (CNN) was developed.
  • Mathematical models of AC motors were used to estimate motor speed and torque output.
  • CNN was employed to compensate for mechanical model inaccuracies.

Main Results:

  • The proposed model demonstrated good consistency with actual dynamometer data.
  • Accurate estimation of motor speed and torque was achieved using electrical parameters.
  • The CNN effectively compensated for errors stemming from pumping unit mechanism defects.

Conclusions:

  • The motor electrical parameter and CNN-based model offers a reliable alternative for acquiring dynamometer data.
  • This method enhances the accuracy of well failure diagnosis in oil field operations.
  • The integration of AI with motor modeling provides a significant advancement in oil production monitoring.