Scholarlink

<p>To solve the problem of diagnosing the working environments of oilfield pumping units, this study proposes an intelligent diagnosing approach built on improved ResNet and optimized AM. Using residual network as the basic network structure, the intermediate layer is extended and improved to extract deep features of pumping unit operation data, and an optimized attention mechanism is introduced to enhance the expression ability of key features. Comparative experiments showed that the algorithm achieved an accuracy of 97.4%, an F1 value of 0.85, and a convergence time of 3.3&#xa0;s. In practical applications in oil fields, this model has increased monthly production by 21.5%, reduced maintenance costs by 25%, and increased net income by 6,000 yuan per unit, effectively improving oil field production efficiency and economic benefits. The results indicate that the research approach has improved the reliability of equipment operation and significantly optimized resource utilization, providing strong support for intelligent management of oil fields and having broad application prospects and promotion value.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design of Intelligent Diagnosis Method for Oilfield Pumping Unit Operating Conditions Combining Improved ResNet and Optimized Attention Mechanism

  • 期刊论文

  • 发表日期：

    2026年3月9日

查看全文
  • Yashun Zhang,
  • Guorong Li,
  • Chunxue Zhao,
  • Min Zhou,
  • Changzheng Si

摘要

To solve the problem of diagnosing the working environments of oilfield pumping units, this study proposes an intelligent diagnosing approach built on improved ResNet and optimized AM. Using residual network as the basic network structure, the intermediate layer is extended and improved to extract deep features of pumping unit operation data, and an optimized attention mechanism is introduced to enhance the expression ability of key features. Comparative experiments showed that the algorithm achieved an accuracy of 97.4%, an F1 value of 0.85, and a convergence time of 3.3 s. In practical applications in oil fields, this model has increased monthly production by 21.5%, reduced maintenance costs by 25%, and increased net income by 6,000 yuan per unit, effectively improving oil field production efficiency and economic benefits. The results indicate that the research approach has improved the reliability of equipment operation and significantly optimized resource utilization, providing strong support for intelligent management of oil fields and having broad application prospects and promotion value.