Artificial Intelligence Applied in Sucker Rod Pumping Wells:IntelligentDynamometer Card Generation,Diagnosis,and Failure Detection UsingDeep Neural Networks

摘要

For most of the mature fields,the oil well operation and maintenance expenditures continue to put financialpressure on the operators in the low oil price period.Digital oilfields and artificial intelligent technologyare the major areas invested to fight for declining oil production and increasing cost.This paper providesa novel artificial intelligent method to monitoring and diagnose the sucker-rod pumping wells using deeplearning algorithms.Traditional method using load and displacement sensors to measure the dynamometer card needs largeinvestment on the equipment installation and maintenance.We build a general model that generates thedynamometer card from electrical parameter using state-of-art deep learning algorithms.The deep learningalgorithms can analyze the relationships between the electrical data and corresponding dynamometer card indifferent conditions,which is very hard for human being to detect.In addition,we build another automateddiagnosis deep learning model from thousands of dynamometer cards labeled with different classifications.We have already tested these newly developed artificial intelligent models on hundreds of sucker rodpumped wells in different oilfields in PetroChina.The field test results show that the dynamometer cardsgenerated from electrical data have above 90% similarity compared to the real dynamometer,which meetthe requirement for well diagnosis.The card generation model is stable and prevents the disturbance ofhostile environment change and sensor failures.The automated diagnosis model also proved to be a goodsubstitute to the conventional software,with above 95% prediction accuracy.The automated diagnosismodel reduces the liability and uncertainty of traditional diagnosis software and can integrated with theformer dynamometer card generation model to fulfill well monitoring and diagnosis automatically withoutany physical model based calculations.These models developed with artificial intelligent technology will be important components in the"Intelligent Fields".They can also be embedded in the IIoT edge computing machines for automaticdiagnosis and control.For ultra-low production wells and the newly producing wells utilized this method,operator can save expenditure and human resources tremendously.