Identifying Slug Formation in Oil Well Pipelines: A Use Case from Industrial Analytics

TL;DR

This paper introduces an interactive, real-time data-driven system for detecting slug formation in oil pipelines, combining visualization, user labeling, and machine learning for practical industrial use.

Contribution

It presents a novel, user-friendly interface integrating data exploration, model training, and real-time alerts for slug detection, bridging research and industrial application.

Findings

Effective real-time slug detection with persistence-based alerts.

Seamless workflow from data upload to live inference.

Enhanced interpretability and usability for industrial decision-making.

Abstract

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.