This paper presents the results of using autoencoder-derived features, rather than hand-crafted features, for predicting rod pump well failures using Support Vector Machines (SVMs). Features derived from dynamometer card shapes are used as inputs to the SVM 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, we demonstrate that these automatically derived abstract features capture high-level card shape characteristics that are orthogonal to the hand-crafted features. In addition, we provide experimental results showing improved well failure prediction accuracy by replacing the hand-crafted features with more informative abstract features.
Number of Pages
Liu, Yintao. Failure Prediction for Rod Pump Artificial Lift Systems. ProQuest, UMI Dissertations Publishing, 2013.
Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science 313.5786 (2006): 504-507.
Bellman, Richard, et al. Adaptive control processes: a guided tour. Vol. 4. Princeton: Princeton University Press, 1961.
Hinton, Geoffrey E. Training products of experts by minimizing contrastive divergence. Neural computation 14.8 (2002): 1771-1800.
Looking for more?
Some of the OnePetro partner societies have developed subject- specific wikis that may help.