ALSTMResNet: Active-Learning-Enhanced LSTMResNet as an Efficiency-Oriented Training Framework for Well Anomaly Monitoring Under Partial Labels

Abstract

Oil-well anomaly monitoring supports safe and efficient oil-and-gas production, but delayed recognition of abnormal operating states can reduce lifting efficiency, trigger costly interventions, and increase operational risk. Existing data-driven detectors are also vulnerable to optimistic estimates when segmentation, normalization, and train–test splitting leak information across source files. This study presents ALSTMResNet, an application-oriented active-learning workflow built on an LSTMResNet backbone for one-step-ahead anomaly screening under partial labels. The workflow converts real 3W well records into leakage-aware file-wise splits, train-only standardized sliding windows, and a fixed 15-channel representation that combines process measurements with temporal covariates; active learning is used only during training to select additional labels while leaving the deployed backbone unchanged. Experiments on the retained benchmark split obtain an F1-score of 0.9354, and five repeated file-wise trials give an average F1-score of 0.8661±0.0666 while reducing retraining time relative to the full-data backbone. Label-budget and acquisition-policy analyses show that the workflow remains competitive under constrained labels, although uncertainty, entropy, margin, least-confidence, and random querying have limited separation in the present binary setting. These results indicate that ALSTMResNet can support cost-aware oil-well anomaly monitoring when labels are partially available and retraining resources are constrained.