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.
SRP Diagnosis
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.
×
Restricted Content
We're sorry, but this content is reserved for SPE Members. If you are a member, please sign in at the top of the page for access. If you are not a member and you find JPT content valuable, we encourage you to become a part of the SPE member community.