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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.00851 |
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| _version_ | 1866915592266579968 |
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| author | Patange, Abhishek Chidambaran, Sharat Shankar, Prabhat B., Manjunath G. Chatterjee, Anindya |
| author_facet | Patange, Abhishek Chidambaran, Sharat Shankar, Prabhat B., Manjunath G. Chatterjee, Anindya |
| contents | Slug formation in oil and gas pipelines poses significant challenges to operational safety and efficiency, yet existing detection approaches are often offline, require domain expertise, and lack real-time interpretability. We present an interactive application that enables end-to-end data-driven slug detection through a compact and user-friendly interface. The system integrates data exploration and labeling, configurable model training and evaluation with multiple classifiers, visualization of classification results with time-series overlays, and a real-time inference module that generates persistence-based alerts when slug events are detected. The demo supports seamless workflows from labeled CSV uploads to live inference on unseen datasets, making it lightweight, portable, and easily deployable. By combining domain-relevant analytics with novel UI/UX features such as snapshot persistence, visual labeling, and real-time alerting, our tool adds significant dissemination value as both a research prototype and a practical industrial application. The demo showcases how interactive human-in-the-loop ML systems can bridge the gap between data science methods and real-world decision-making in critical process industries, with broader applicability to time-series fault diagnosis tasks beyond oil and gas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Slug Formation in Oil Well Pipelines: A Use Case from Industrial Analytics Patange, Abhishek Chidambaran, Sharat Shankar, Prabhat B., Manjunath G. Chatterjee, Anindya Machine Learning Slug formation in oil and gas pipelines poses significant challenges to operational safety and efficiency, yet existing detection approaches are often offline, require domain expertise, and lack real-time interpretability. We present an interactive application that enables end-to-end data-driven slug detection through a compact and user-friendly interface. The system integrates data exploration and labeling, configurable model training and evaluation with multiple classifiers, visualization of classification results with time-series overlays, and a real-time inference module that generates persistence-based alerts when slug events are detected. The demo supports seamless workflows from labeled CSV uploads to live inference on unseen datasets, making it lightweight, portable, and easily deployable. By combining domain-relevant analytics with novel UI/UX features such as snapshot persistence, visual labeling, and real-time alerting, our tool adds significant dissemination value as both a research prototype and a practical industrial application. The demo showcases how interactive human-in-the-loop ML systems can bridge the gap between data science methods and real-world decision-making in critical process industries, with broader applicability to time-series fault diagnosis tasks beyond oil and gas. |
| title | Identifying Slug Formation in Oil Well Pipelines: A Use Case from Industrial Analytics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.00851 |