Real-time monitoring of CO2 transport pipelines using deep learning

Abstract

Real-time pipeline monitoring is important for the safe transportation of captured CO2. A dynamic modeling method, which is one of pipeline monitoring methods, can provide reliable diagnostic results for various anomalies. In the dynamic modeling method, anomalies are detected by comparing the predictions and observations of pipeline monitoring variables. However, the licensing costs associated with the use of pipeline flow simulators that provides the predictions are high. In this study, we developed a real-time deep-learning-based pipeline monitoring method that can save the licensing cost of the pipeline flow simulators. The predictions are obtained using deep-learning models where the pipeline flow simulator is required only in the training step. Two improvements were made to enhance both the prediction and anomaly detection accuracies. First, the prediction accuracy for the monitoring variables can be improved by considering a delay time interval between inlet and outlet points in pairing training input and output data. Second, the anomaly detection accuracy can be also improved by conditionally choosing observations based on the normal operation ranges of the observations. As part of a field demonstration, the proposed deep-learning-based pipeline monitoring method was applied to the monitoring of a CO2 transport pipeline located in the East Sea gas field. The results showed the anomaly detection accuracy of the proposed method was improved by more than 25 %.