Time series prediction of hydrate dynamics on flow assurance using PCA and Recurrent neural networks with iterative transfer learning

Preventing gas hydrate formation is critical in offshore gas and oil production systems. Several models can predict hydrate formation, however, these empirical approaches have limitations due to dependency on geometries and fluid characteristics of the systems. The trends of hydrate formation or risk are considered statistical, which means there is no definite model to describe its behavior. Herein, we present a novel framework based on a combination of feature reduction methods and several deep learning models to predict the hydrate formation trend through the multivariate sensor data. Transition and segregation trends during hydrate formation were predicted in real-time using sequential time series data from the last 60 s. We employed various deep learning models (Dense, LSTM, GRU, ARLSTM), layers, and dropout to investigate and enhance the prediction ability of each model. Two groups of experimental data (200 rpm, 600 rpm) were used for training and testing the prediction to examine the universal applicability of the model. Transfer learning in training the model was employed to apply the discrete experimental set into time-series data and enhance the accuracy. The results with higher layer numbers and a dropout rate of 0.2 ∼ 0.6 showed the best performance. ARLSTM showed the smallest error among deep learning models and predicted the good trend of kinetic characteristics (transition and segregation part) during the hydrate formation. This approach based on deep learning can be adopted for risk and issue detection of pipelines in the gas production system.