The development of leak detection model in subsea gas pipeline using machine learning

Pipelines are mainly used to transport crude and refined petroleum, such as natural gas, worldwide. Monitoring pipeline health condition at offshore locations is challenging. Despite several attempts to develop leak detection systems, few can simultaneously detect the leak location and size. It is extremely difficult to obtain abnormal data such as actual leaks from a long-distance subsea pipeline. Dynamic modeling can be a good alternative to overcome this limitation. In this study, based on the dynamic model matched with the field, we conducted various flow simulations and selected the most sensitive variables. By changing these variables within an appropriate range, a machine-learning data set was generated. We used deep neural network methods to train the data and derived the optimal learning model. To improve the model accuracy, we adjusted the pipeline model section size not to exceed 20 m from the initial 50 m and designed models with a more detailed pipeline structure. The mean absolute error for each leak size was separately calculated to assess its effect on learning itself. Overall, the model showed excellent accuracy. However, for leak sizes of 0.5 cm, the accuracy appeared too low because the leak effect on mass flow, pressure, and the temperature was minimal. These parameters have been reported to have a great impact on the accuracy of machine-learning models. Therefore, the leak size detected was rearranged to perform data learning again. As a result, the model accuracy was improved by 80% compared to the initial learning model. Based on our study results, we proposed a flowchart for leak detection in the gas pipeline. The proposed procedure can be applied to various pipelines and support more efficient operation by detecting leaks in real-time.