Predicting methane solubility in water and seawater by machine learning algorithms: Application to methane transport modeling

Abstract

The upward migration of methane from natural gas wells associated with fracking operations may lead to contamination of groundwater resources and surface leakage. Numerical simulations of methane transport in the subsurface environment require knowledge of methane solubility in the aqueous phase. This study employs machine learning (ML) algorithms to predict methane solubility in aquatic systems for temperatures ranging from 273.15 to 518.3 K and pressures ranging from 1 to 1570 bar. Four regression algorithms including regression tree (RT), boosted regression tree (BRT), least square support vector machine (LSSVM) and Gaussian process regression (GPR) were utilized for predicting methane solubility in pure water and mixed aquatic systems containing Na+, K+, Ca2+, Mg2+, Cl− and SO4-2. The experimental data collected from the literature were used to implement the models. We used Grid search (GS), Random search (RS) and Bayesian optimization (BO) for tuning hyper-parameters of the ML models. Moreover, the predicted values of methane solubility were compared against Spivey et al. (2004) and Duan and Mao (2006) equations of state. The results show that the BRT-BO model is the most rigorous model for the prediction task. The coefficient of determination (R2) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 1.19 × 10−7. The performance of the BRT-BO model is satisfactory, showing an acceptable agreement with experimental data. The comparison results demonstrated the superior performance of the BRT-BO model for predicting methane solubility in aquatic systems over a span of temperature, pressure and ionic strength that occurs in deep marine environments.