Research on Pipeline Hydrate Deposition Prediction Based on Neural Networks

With the continuous advancement of oil and gas exploration into the deep sea, polar regions, and other regions, the transportation pipelines of oil and gas have become increasingly important. However, the high-pressure and low-temperature environment makes pipelines prone to hydrate blockage, which can cause severe damage to oil and gas transportation pipelines and significant economic losses. Therefore, predicting the hydrate deposition situation in pipelines to formulating corresponding flow assurance measures is crucial. This article uses OLGA software and combines actual operating conditions to establish a hydrate deposition model to simulate the hydrate deposition situation of the pipeline, and the Elman neural network prediction method is built to predict the volume fraction of pipeline hydrates, which fluid temperature, ambient temperature, outlet pressure, inlet pressure, flow rate, and heat transfer coefficient as input parameters, the maximum hydrate volume fraction in the pipeline as the output parameter. In addition, the Elman neural network prediction method was optimized using the genetic algorithm to improve prediction accuracy. The results show that the correlation coefficient of the predicted and actual values is 0.9719, and the RMSE, MAE, MSE of the GA-Elman neural network prediction model are 0.0293, 0.0267, and 0.0008, respectively, which can accurately predict pipeline hydrate deposition and providing some reference and guidance for pipeline hydrate prevention and control.