Accurate determination of CO2 and H2S pure
gas and mixture solubility of CO2 and H2S in
water and brine is important to predict the chemical reactions, phase
behavior, and solubility trapping of the sour gases in petroleum reservoir
engineering and underground carbon storage. In this study, three machine
learning (ML) models, backpropagation neural networks (BPNN), generalized
regression neural network (GRNN), and eXtreme Gradient Boosting (XGBoost)
models, were implemented to predict gas solubility. In addition, a
fourth model called fusion model has been developed for higher accuracy
by stacking the three aforementioned models. The models were trained
and validated with a database of experimental data with a wide range
of temperature, pressure, NaCl salinity, and initial H2S concentration (2784 data points). The results from the four models
are highly consistent and agree well with the experimental data. Among
these models, the fusion model shows the best performance in estimating
the gas solubility with a root-mean-square error (RMSE) and an adjusted R
2 of 0.0271 and 0.9995, respectively, which
are better than previous ML and correlation methods in the literature.
In addition, a software was developed in this study for visually analyzing
the gas solubility trends with different variables. Sensitivity analyses
show that pressure and temperature are the most sensitive parameters.
The gas solubility in water and brine increases monotonically with
temperature, but logarithmically with increasing pressure. The results
predict that the CO2 + H2S mixture is more soluble
than the pure CO2. Salinity has an inhibitory effect on
the solubility. At high salinity, the solubility increases with increasing
pressure or temperature compared to that at low salinity. The CO2 and H2S solubility modeling is essential for several
applications such as reservoir souring, CO2 sequestration,
CO2-enhanced oil recovery (CO2-EOR), thermal
recovery/steam-assisted gravity drainage via aquathermolysis, and
corrosion-related issues in sour gas fields. A library of charts and
tables of solubility data were generated for the relevant gas solubility
in a wide range of conditions, which provides a ready reference for
geoscientists and chemical and petroleum engineers to acquire CO2 and H2S solubilities at desired conditions.