Abstract
During the production of hydrocarbons, offshore platforms frequently leak large volumes of oil and gas. Traditional methods for detecting these leaks are prone to errors, making them inefficient at detecting leaks precisely. These leaks, however, have damaging effects on the environment and humans, posing economic risks to companies as well. This study explores the application of artificial intelligence to monitor pump leaks. The data used in this work was obtained from Kaggle, which contained sensor readings from pumps. Supervised (random forest, support vector machine, and naïve bayes) and unsupervised (isolation forest) machine learning algorithms were employed for leak detection. The results showed that supervised machine learning algorithms were more accurate, with random forest having the greatest F1-score (0.993). Leveraging artificial intelligence for leak monitoring proved effective, offering a promising alternative to traditional methods.
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