Deep learning-based modeling of CO₂ corrosion rate prediction in oil and gas pipelines

In recent years, with the continuous development of oil and gas resources, oil and gas pipeline construction scale continues to expand to bring considerable economic benefits at the same time, the corresponding oil and gas resources transportation problems also ensue, pipeline corrosion caused by the leakage problem is the most serious and most harmful. Therefore, this study starts from the perspective of engineering safety and conducts research on the prediction of CO₂ corrosion rates in oil and gas pipelines. This paper first analyzes three relatively new deep learning models, such as deep confidence network, generative adversarial network, and transformer model. According to the corrosion rate mechanism analysis, select the corresponding corrosion factor, and data expansion of the corrosion data set, and then use the correlation coefficient method to assess the correlation between the corrosion factor indicators, the use of principal component analysis to identify the main features of corrosion, to lay a good data foundation for the subsequent model input. This paper also uses the Adam optimization algorithm to improve the DBN network, and constructs a corrosion rate prediction model based on the improved DBN. Through the prediction effect detection, the maximum value of the relative error of the CO₂ corrosion rate prediction results of the model in this paper does not exceed 3%, the error is small, and the prediction effect is good. Compared to the traditional DBN model, generative adversarial network, and Transformer model, optimal results are achieved.