Toward a Robust, Universal Predictor of Gas Hydrate Equilibria by Means of a Deep Learning Regression.

Due to offshore reservoirs being developed in ever deeper and colder waters, gas hydrates are increasingly becoming a significant factor when considering the profitability of a reservoir due to flow disruptions, equipment, and safety hazards arising from the hydrate plug formation. Due to low-dosage hydrate inhibitors such as kinetic inhibitors competing with traditional thermodynamic inhibitors such as methanol, accurate information regarding the hydrate equilibrium conditions is required to determine the optimal hydrate control strategy. Existing thermodynamic models can prove inflexible regarding parameter adjustment and the incorporation of new data. Developing a multivariate regression model capable of generalizing hydrate equilibria over a wide range of conditions, with results competing with thermodynamic models is worthwhile. A multilayer perceptron neural network of three hidden layers has undergone supervised training means of a backpropagation to accurately predict uninhibited hydrate equilibrium pressure for a range of gas mixtures with nine input features, excluding hydrogen sulfide and electrolytes, from a dataset of 1209 equilibrium points, 670 of which are multicomponent gases, sampled in a rigorous data sampling campaign from existing experimental studies. Statistical significance of results has been emphasized, with models validated using 10-fold cross-validation and holdout validation, facilitating hyperparameter optimization without overfitting, while stratified holdout ensures testing a wide range of conditions. The developed model has proven to outperform two popular thermodynamic models. Various scoring metrics are used, with an average cross-validated of 0.987 ± (0.003). An of 0.993 and mean absolute percentage error of 5.56% are yielded for holdout validation. Auxiliary models are included to determine the multicomponent prediction capability and dependency on individual data sources. Multicomponent data prediction has proven successful; results prove that the model accurately generalizes hydrate equilibria and is well suited to predicting unseen data. Positive results are largely insensitive to exact model parameters, thus indicating a robust, replicable methodology.