4.6 Article

Intelligent Diagnosis Model of Working Conditions in Variable Torque Pumping Unit Wells Based on an Electric Power Diagram

期刊

PROCESSES
卷 11, 期 4, 页码 -

出版社

MDPI
DOI: 10.3390/pr11041166

关键词

dynamometer card; electric power diagram calculation; eigenvalue extraction; intelligent; working condition diagnosis

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This paper proposes a diagnosis model of working conditions based on feature recognition, which solves the problems of the lack of an electric power diagram atlas under different working conditions and the difficulty in intelligent diagnosis of variable torqued pumping unit wells. The mathematical relationship model between the polished rod load and motor output power is derived, and the electric power diagram can be calculated based on a dynamometer card. The electric power diagram atlas is created, and feature analysis is carried out to realize the direct diagnosis of the working conditions in the variable torque pumping unit wells.
Because of the problems, such as the lack of an electric power diagram atlas under different working conditions and the difficulty in intelligent diagnosis of variable torqued pumping unit wells, this paper proposes a diagnosis model of working conditions based on feature recognition. The mathematical relationship model between the polished rod load and motor output power is derived based on the analysis of geometric structure, motion law, and process of energy transformation and transfer of the variable torque pumping unit. It can calculate the electric power diagram based on a dynamometer card. On this basis, the electric power diagram atlas is created, and the feature analysis and eigenvalue extraction of the electric power diagrams under different working conditions are carried out to realize the direct diagnosis of the working conditions in the variable torque pumping unit wells. The application and analysis of examples show that the electric power diagram atlas created in this paper has good practicability, and the working condition diagnosis model accuracy is high. It can provide a theoretical basis and technical support for the intelligent diagnosis of oil production working conditions and improve the intellectual management level of the oilfield, which is conducive to reducing production management costs and improving the oilfield's production efficiency and benefits.

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