Accurate diagnosis of oil well operating conditions is the key to achieve safe and efficient production and enhanced oil recovery. With the promotion of oilfield information construction, real-time collection of production dynamic monitoring data has covered a wide range, but how to effectively tap the potential value of massive data is still a difficult problem to be solved. Combined with the advanced advantages of big data technology and deep learning model, intelligent diagnosis technology is showing the potential to break through the limitations of traditional condition diagnosis methods. Based on more than 40 million sets of historical monitoring data covering multiple types of oil reservoirs, this study constructed a high-quality diagnostic sample set containing 5 classes of 37 working conditions, and designed a special convolutional neural network model OWDNet according to the characteristics of oil well working conditions. The model has a 26-layer network structure and more than 59 million learnable parameters, and after optimized training, it achieves an excellent performance of 99.7% training accuracy and 98.9% verification accuracy. In the actual oilfield application, the intelligent diagnosis system developed based on OWDNet has completed more than 5 million working conditions identification, and the field diagnosis accuracy rate has reached 90%, and the real-time alarm push has been significantly improved. As a result of the continuous operation of the system, well production management has been refined, and the proportion of continuously stable production Wells has increased from 68% to 88%. The research shows that the deep integration of deep learning and big data not only accelerates the intelligent transformation of oil fields, but also provides strong support for the construction of a more efficient and reliable oil field production management system, and sets a new benchmark for industry technology upgrading and innovative application.

Yuxin Wang. Research on Intelligent Diagnosis Technology of Oil Well Condition Based on Big Data and Deep Learning Model OWDNet. Academic Journal of Architecture and Geotechnical Engineering (2025), Vol. 7, Issue 1: 17-22. https://doi.org/10.25236/AJAGE.2025.070103.

[1] Wu Y, Ren X, Cao Z,et al. Diagnosis and analysis of repeatable diseases based on deep intelligent model driven by big data of medical images[J]. Digital Communications and Networks, 2021, (2). DOI:10.1016/j.dcan.2021.04.004.

[2] Wosowei J B, Shastry C. An Efficient Intelligent Oil Well Monitoring System for Niger Delta Oil Fields[J]. International Journal of Advanced Research in Science, Communication and Technology, 2023. DOI: 10.48175/ijarsct-9563.

[3] Deng Z K, Chang P X, Zhou Z P,et al. Application of Intelligent Rodless Artificial Lift Technology in Shale Oil of Changqing Oilfield[C]. International Petroleum and Petrochemical Technology Conference. Springer, Singapore, 2023. DOI:10.1007/978-981-99-2649-7_47. 

[4] Huang Z, Li K, Ke C,et al. An intelligent diagnosis method for oil-well pump leakage fault in oilfield production Internet of Things system based on convolutional attention residual learning[J]. Engineering Applications of Artificial Intelligence, 2023. DOI:10.1016/j.engappai.2023.106829.