Predicting formation conditions of gas hydrates in salt water is important for the management of hydrate in processes such as flow assurance, deep-water drilling, and hydrate-based technology development. This paper applied and compared five machine learning algorithms to develop prediction tools for the estimation of methane hydrate formation temperature in the presence of salt water. These machine learning algorithms are Multiple Linear Regression, k-Nearest Neighbor, Support Vector Regression, Random Forest, and Gradient Boosting Regression. In total, 702 experimental data points in literature from 1951 to 2020 were collected for modeling purposes. The experimental data span salt concentrations up to 29.2 wt% and pressures up to 200 MPa. Among these five machine learning methods, Gradient Boosting Regression gives the best prediction with $R^2=0.998$ and AARD = 0.074%. Thus, the methods of Gradient Boosting Regression function as an accurate tool for predicting the formation conditions of methane hydrates in salt water.

预测盐水中天然气水合物的形成条件对于流程保证中的水合物管理（例如流量保证，深水钻探和基于水合物的技术开发）非常重要。本文应用并比较了五种机器学习算法来开发预测工具，以评估存在盐水时甲烷水合物的形成温度。这些机器学习算法是多元线性回归k-最近邻居，支持向量回归，随机森林和梯度增强回归。总共收集了1951年至2020年文献中的702个实验数据点用于建模。实验数据涵盖了盐浓度最高为29.2 wt％，压力最高为200 MPa。在这五种机器学习方法中，梯度提升回归可通过以下方式提供最佳预测：${\mathrm{[R}}^{\mathrm{2个}}=0.998$和AARD  = 0.074％。因此，梯度增强回归方法可作为预测盐水中甲烷水合物形成条件的准确工具。