Enhancing Plunger Lift Anomaly Detection: A Vision Transformer-Based Approach Leveraging Pretrained Models and Graphic Data Augmentation

Plunger lift systems are vital for optimizing production in gas wells, but their performance can be compromised by various operational anomalies. Traditional diagnostic methods and conventional Convolutional Neural Network (CNN) approaches often struggle with the complex, transient data from these systems, particularly in capturing long-range temporal dependencies and generalizing from limited, imbalanced datasets. This study presents an enhanced diagnostic framework for plunger lift anomaly detection by leveraging the strengths of a pre-trained Vision Transformer (ViT). The methodology involves transforming one-dimensional time-series pressure data into two-dimensional image representations using Gramian Angular Fields (GAF), making them suitable for analysis by vision models. The ViT model, initialized with pre-trained weights, is further optimized using Bayesian Optimization (BO) for hyperparameter tuning, and a tailored data augmentation pipeline is employed to improve robustness. Comparative evaluations demonstrate that the proposed ViT-based approach, particularly the ViT+GAF+BO model, significantly outperforms baseline CNN models and their optimized variants, achieving the highest Precision, Recall, and F1-score of 0.93. Visualizations using t-SNE confirm the ViT's superior capability in learning discriminative features, showcasing well-separated clusters for different operational conditions compared to CNNs. This research underscores the potential of pre-trained ViTs combined with appropriate data representation and optimization techniques for achieving accurate and reliable anomaly detection in plunger lift systems.