In this paper, deep learning artificial neural networks (ANNs) are used to analyze the features of downhole dynamometer cards and identify the sucker rod pumping system conditions. A description model for the dynamometer cards, using Fourier descriptors, was established for card feature extraction. Then, neural networks were trained to generate failure prediction models to recognize downhole faults of the rod pumping systems. The failure prediction models were validated and tested with a large database of previously interpreted cards.
The proposed model is trained by using 4,467 dynamometer cards—29.2% of these cards represent sucker rod pumping systems of normal conditions, while the rest (70.8%) represent faulty sucker rod pumping systems. Genetic algorithms (GAs) were used to search for the best deep ANN structure that gives highest accuracy for the testing data. Accuracy of the proposed ANN model was measured with 1,915 cards that were not used in developing the ANN. The proposed model identified the sucker rod system failure successfully with very high accuracy (99.69%).