Analytic Solutions Help Predict Sucker-Rod-Pump Failure
The authors of this paper present a method for prediction of sucker-rod-pump failure based on improved, completely connected perceptron artificial neural networks.
A method for prediction of sucker-rod-pump (SRP) failure based on improved, completely connected perceptron (ICCP) artificial neural networks (ANNs) is presented. Results are compared with historical wellbore data. The validity of the ANN model is proved to meet SRP failure prediction with a ±5% error, helping identify and prevent potential damage, reduce costs and risks, and optimize production.
To evaluate the performance of SRPs, dynamometer cards are used. The load on the top rod is measured and plotted in relation to the polished rod position as the pumping unit moves through each stroke cycle.
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