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Multisource working condition recognition via nonlinear kernel learning and p-Laplacian manifold learning.

Bin Zhou1, Rui Niu2, Shuo Yang1

  • 1School of Computer Science and Technology, Shandong University of Technology, Zibo, China.

Heliyon
|March 7, 2024
PubMed
Summary

This study introduces a new method for recognizing oil production states using limited data. The approach enhances recognition accuracy and practical application in challenging energy environments.

Keywords:
Measured signal featureMultisource working condition recognitionNonlinear kernel learningP-Laplacian manifold learningSucker-rod pumping energy system

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Area of Science:

  • Petroleum Engineering
  • Machine Learning
  • Data Science

Background:

  • Sucker-rod pumping systems are crucial for oil production.
  • Accurate operating condition recognition is vital for efficiency.
  • Current methods struggle with limited labeled data and multisource information.

Purpose of the Study:

  • To develop an advanced operating state recognition scheme for sucker-rod pumping systems.
  • To improve recognition accuracy and engineering practicability using multisource data and fewer samples.
  • To address the challenges of data scarcity in energy environment scientific applications.

Main Methods:

  • Utilized multisource nonlinear kernel learning and p-Laplacian high-order manifold regularization logistic regression.
  • Extracted three key features: wellhead temperature, electrical power, and ground dynamometer cards.
  • Developed a recognition model based on the proposed algorithm.

Main Results:

  • The proposed scheme demonstrated superior performance compared to traditional methods.
  • Achieved greater recognition effect and model robustness with fewer labeled samples.
  • Validated effectiveness using experimental data from 60 wells in a Chinese oil field.

Conclusions:

  • The multisource p-Laplacian regularization kernel logistic regress algorithm offers significant improvements in operating condition recognition.
  • The method is highly effective and practical, especially when dealing with limited labeled data.
  • This research advances the application of machine learning in the oil production industry.