Gaussian process regression to determine water content of methane: Application to methane transport modeling

The uncontrolled release of methane from natural gas wells may pose risks to shallow groundwater resources. Numerical modeling of methane migration from deep hydrocarbon formations towards shallow systems requires knowledge of phase behavior of the water-methane system, usually calculated by classic thermodynamic approaches. This study presents a Gaussian process regression (GPR) model to estimate water content of methane gas using pressure and temperature as input parameters. Bayesian optimization algorithm was implemented to tune hyper-parameters of the GPR model. The GPR predictions were evaluated with experimental data as well as four thermodynamic models. The results revealed that the predictions of the GPR are in good correspondence with experimental data having a MSE value of 3.127 x 10(-7) and R-2 of 0.981. Furthermore, the analysis showed that the GPR model exhibits an acceptable performance comparing with the well-known thermodynamic models. The GPR predicts the water content of methane over widespread ranges of pressure and temperature with a degree of accuracy needed for typical subsurface engineering applications.

Automated summary

This study successfully predicts the water content of methane gas using a Gaussian process regression (GPR) model, showing good consistency with experimental data and thermodynamic models. The results demonstrate that the GPR model achieves the accuracy needed for typical subsurface engineering applications across a wide range of pressure and temperature.