Natural Gas Hydrate Formation Pressure Prediction Based on Machine Learning

Natural gas hydrate formation and accumulation in pipelines may increase pressure drop and, in severe cases, cause blockage, thereby affecting safe pipeline operation. Hydrate formation pressure represents the pressure boundary at which hydrates begin to form stably under a given temperature and gas composition, and is an important parameter for assessing whether the operating pressure is approaching hydrate formation conditions. Therefore, accurate prediction of hydrate formation pressure is important for pipeline hydrate risk assessment. This study develops an optimized least-squares support vector regression model to approximate CSMHYD-calculated hydrate formation pressures for methane–ethane–propane mixtures. Two base models, a backpropagation neural network and a least-squares support vector regression model, are established. The least-squares support vector regression model achieves an R2 of 0.8883 and outperforms the backpropagation neural network. On this basis, particle swarm optimization and improved particle swarm optimization are used to optimize the parameters of the least-squares support vector regression model. Across 15 runs, IPSO-LSSVR achieves a mean R2 of 0.9602, indicating a modest average improvement over PSO-LSSVR. These errors quantify agreement with CSMHYD calculations, not with measured hydrate equilibria.