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An Exploratory Study on Workover Scenario Understanding Using Prompt-Enhanced Vision-Language Models

Author

Listed:
  • Xingyu Liu

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Liming Zhang

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Zewen Song

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Ruijia Zhang

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Jialin Wang

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Chenyang Wang

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Wenhao Liang

    (State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

As oil and gas exploration has deepened, the complexity and risk of well repair operations has increased, and the traditional description methods based on text and charts have limitations in accuracy and efficiency. Therefore, this study proposes a well repair scene description method based on visual language technology and a cross-modal coupling prompt enhancement mechanism. The research first analyzes the characteristics of well repair scenes and clarifies the key information requirements. Then, a set of prompt-enhanced visual language models is designed, which can automatically extract key information from well site images and generate structured natural language descriptions. Experiments show that this method significantly improves the accuracy of target recognition (from 0.7068 to 0.8002) and the quality of text generation (the perplexity drops from 3414.88 to 74.96). Moreover, this method is universal and scalable, and it can be applied to similar complex scene description tasks, providing new ideas for the application of well repair operations and visual language technology in the industrial field. In the future, the model performance will be further optimized, and application scenarios will be expanded to contribute to the development of oil and gas exploration.

Suggested Citation

  • Xingyu Liu & Liming Zhang & Zewen Song & Ruijia Zhang & Jialin Wang & Chenyang Wang & Wenhao Liang, 2025. "An Exploratory Study on Workover Scenario Understanding Using Prompt-Enhanced Vision-Language Models," Mathematics, MDPI, vol. 13(10), pages 1-27, May.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1622-:d:1656383
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