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

Xingyu Liu, Liming Zhang (), Zewen Song, Ruijia Zhang, Jialin Wang, Chenyang Wang and Wenhao Liang
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Xingyu Liu: State Key Laboratory of Deep Oil and Gas, 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
Zewen Song: State Key Laboratory of Deep Oil and Gas, 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
Jialin Wang: State Key Laboratory of Deep Oil and Gas, 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
Wenhao Liang: State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China

Mathematics, 2025, vol. 13, issue 10, 1-27

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.

Keywords: workover operation; vision language model; prompt learning; scenario description; industrial large model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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