Hyperparameter Tuning and Feature Selection for Improving Flow Instability Detection in Offshore Oil Wells

Flow instability is a class of abnormal operation in subsea oil wells. Applying machine learning models and improving detection and classification performance is decisive to reduce operational costs and downtime. In this paper we evaluate a pipeline of methods in order to increase correct classification rates. Our strategy is defined to avoid the similarity bias and approaches the binary problem in two distinct ways: A) using normal labels as negative and B) using both normal labels and all other kind of defects as negative, leveraging all data in the available dataset. The workflow includes feature extraction, hyperparameter tuning, feature selection with sequential algorithms, hybrid ranking wrapper and also with genetic algorithm. We show that hyperparameter tuning produces minor improvements and due to problem complexity a robust feature selection algorithm is required to deliver higher results.