A Fault Diagnosis Method for Oil Well Electrical Power Diagrams Based on Multidimensional Clustering Performance Evaluation

In oilfield extraction activities, traditional downhole condition monitoring is typically conducted using dynamometer cards to capture the dynamic changes in the load and displacement of the sucker rod. However, this method has severe limitations in terms of real-time performance and maintenance cos...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Sensors (Basel, Switzerland) Ročník 25; číslo 6; s. 1688
Hlavní autoři: Liu, Xingyu, Meng, Xin, Hu, Ze, Duan, Hancong, Wang, Min, Chen, Yaping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 08.03.2025
MDPI
Témata:
Accuracy
Algorithms
Analysis
beam-pumping system
Classification
cluster validity function
Clustering
Data processing
Dimensional analysis
Electric power
Electric properties
electrical parameters
Fault diagnosis
FCM clustering algorithm
Machine learning
Mathematical models
Mathematics
Methods
Oil fields
Oil sands
Oil wells
Performance evaluation
Simulation methods
Wavelet transforms
Working conditions
China
beam-pumping system
cluster validity function
electrical parameters
FCM clustering algorithm
ISSN:1424-8220, 1424-8220
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In oilfield extraction activities, traditional downhole condition monitoring is typically conducted using dynamometer cards to capture the dynamic changes in the load and displacement of the sucker rod. However, this method has severe limitations in terms of real-time performance and maintenance costs, making it difficult to meet the demands of modern extraction. To overcome these shortcomings, this paper proposes a novel fault detection method based on the analysis of motor power parameters. Through the dynamic mathematical modeling of the pumping unit system, we transform the indicator diagram of beam-pumping units into electric power diagrams and conduct an in-depth analysis of the characteristics of electric power diagrams under five typical operating conditions, revealing the impact of different working conditions on electric power. Compared to traditional methods, we introduce fourteen new features of the electrical parameters, encompassing multidimensional analyses in the time domain, frequency domain, and time-frequency domain, significantly enhancing the richness and accuracy of feature extraction. Additionally, we propose a new effectiveness evaluation method for the FCM clustering algorithm, integrating fuzzy membership degrees and the geometric structure of the dataset, overcoming the limitations of traditional clustering algorithms in terms of accuracy and the determination of the number of clusters. Through simulations and experiments on 10 UCI datasets, the proposed effectiveness function accurately evaluates the clustering results and determines the optimal number of clusters, significantly improving the performance of the clustering algorithm. Experimental results show that the fault diagnosis accuracy of our method reaches 98.4%, significantly outperforming traditional SVM and ELM methods. This high-precision diagnostic result validates the effectiveness of the method, enabling the efficient real-time monitoring of the working status of beam-pumping unit wells. In summary, the proposed method has significant advantages in real-time performance, diagnostic accuracy, and cost-effectiveness, solving the bottleneck problems of traditional methods and enhancing fault diagnosis capabilities in oilfield extraction processes.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s25061688