AUTOENCODER-DERIVED FEATURES AS INPUTS TO CLASSIFICATION ALGORITHMS FOR PREDICTING FAILURES

摘要

The invention relates to using autoencoder-derived features for predicting well failures (e.g., rod pump failures) using a machine learning classifier (e.g., a Support Vector Machine (SVMs)). Features derived from dynamometer card shapes are used as inputs to the machine learning classifier algorithm. Hand-crafted features can lose important information whereas autoencoder-derived abstract features are designed to minimize information loss. Autoencoders are a type of neural network with layers organized in an hourglass shape of contraction and subsequent expansion; such a network eventually learns how to compactly represent a data set as a set of new abstract features with minimal information loss. When applied to card shape data, it can be demonstrated that these automatically derived abstract features capture high-level card shape characteristics that are orthogonal to the hand-crafted features. In addition, experimental results show improved well failure prediction accuracy by replacing the hand crafted features with more informative abstract features.