Transfer learning-driven prediction of oil and gaspipeline corrosion rates in small sample scenarios.

Abstract

To ensure the safe operation of oil and gas pipeline systems in complex environments, accurately predicting the corrosion rate of natural gas well pipes is of paramount importance. Given the widespread challenge of pipe corrosion in the oil and gas industry, we propose a transfer learning model based on a CNN-LSTM-Transformer architecture with a staged fine-tuning strategy for corrosion rate prediction under small-sample conditions. The model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer modules. CNN is employed to extract local features from corrosion data. LSTM captures the temporal dependencies within the data, and the Transformer module applies multi-head attention to recalibrate features, effectively addressing long-range dependencies. To enhance the model's adaptability, the CNN-LSTM-Transformer model is initially trained on a source domain and then progressively fine-tuned on a target domain to facilitate knowledge transfer. Experimental results demonstrate that, after staged fine-tuning, the CNN-LSTM-Transformer model achieves an MAE of 0.021, RMSE of 0.031, and an R² of 0.909, outperforming other transfer learning approaches by a substantial margin.