Multi-Dimensional Time Series Anomaly Detection Model Based on Transformer and GAN for Oil and Gas Production IoT Applications

This paper introduces the application of temporal data anomaly monitoring and prediction in oil and gas field production data management. By proposing a Transformer-based DMRformer model and a multidimensional time series anomaly detection model based on Transformer and GAN, the discovery of temporal dependencies in complex and unknown temporal patterns and the enhancement of anomaly detection capability are realized. In oil and gas field production data management, these models can be applied to predict the long-term trend of oil and gas field production data, improve the prediction performance, and realize the anomaly detection by capturing the temporal dependencies and amplifying the anomalies in the normal state. In addition, the designed and implemented industrial IoT timing data governance system uses these models to complete timing data prediction, anomaly detection, and related governance tasks, and provides functions such as data integration management, data visualization, and comprehensive monitoring, which facilitates operation and maintenance personnel to understand the field status of the operation area in real time and take timely countermeasures. After practical application verification, these models and systems have high application feasibility in oil and gas field production data management and are expected to provide effective support and guidance for oil and gas field production data management.