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The petroleum industry is crucial for modern society, but the productionprocess is complex and risky. During the production, accidents or failures,resulting from undesired production events, can cause severe environmental andeconomic damage. Previous studies have investigated machine learning (ML)methods for undesired event detection. However, the prediction of eventprobability in real-time was insufficiently addressed, which is essential sinceit is important to undertake early intervention when an event is expected tohappen. This paper proposes two ML approaches, random forests and temporalconvolutional networks, to detect undesired events in real-time. Results showthat our approaches can effectively classify event types and predict theprobability of their appearance, addressing the challenges uncovered inprevious studies and providing a more effective solution for failure eventmanagement during the production.

Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry