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 R2 of 0.987 ± (0.003). An R2 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.