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.

与压裂作业相关的天然气井中的甲烷向上迁移可能导致地下水资源的污染和地表泄漏。地下环境中甲烷传输的数值模拟需要了解甲烷在水相中的溶解度。本研究采用机器学习 (ML) 算法来预测甲烷在水生系统中的溶解度，温度范围为 273.15 至 518.3 K，压力范围为 1 至 1570 巴。四种回归算法包括回归树 (RT)、增强回归树 (BRT)、最小二乘支持向量机 (LSSVM) 和高斯过程回归 (GPR) 用于预测甲烷在纯水和含 Na ⁺、K 的混合水生系统中的溶解度⁺ , 钙²⁺、Mg ²⁺、Cl ^-和SO ₄ ^-2。从文献中收集的实验数据用于实现模型。我们使用网格搜索 (GS)、随机搜索 (RS) 和贝叶斯优化 (BO) 来调整 ML 模型的超参数。此外，将甲烷溶解度的预测值与 Spivey 等人进行了比较。(2004) 和段和毛 (2006)状态方程。结果表明，BRT-BO模型是预测任务最严谨的模型。实验值和预测值之间的决定系数 (R ² ) 为 0.99，均方误差 (MSE) 为 1.19 × 10 ^-7. BRT-BO 模型的性能令人满意，与实验数据显示出可接受的一致性。比较结果表明，BRT-BO 模型在预测水生系统中甲烷在深海环境中发生的温度、压力和离子强度范围内的溶解度方面具有卓越的性能。