Study of Methane Solubility Calculation Based on Modified Henry’s Law and BP Neural Network

Methane (CH₄), a non-polar molecule characterized by a tetrahedral structure, stands as the simplest organic compound. Predominantly constituting conventional natural gas, shale gas, and combustible ice, it plays a pivotal role as a carbon-based resource and a key raw material in the petrochemical industry. In natural formations, CH₄ and H₂O coexist in a synergistic system. This interplay necessitates a thorough examination of the phase equilibrium in the CH₄-H₂O system and CH_4’s solubility under extreme conditions of temperature and pressure, which is crucial for understanding the genesis and development of gas reservoirs. This study synthesizes a comprehensive solubility database by aggregating extensive solubility data of CH₄ in both pure and saline water. Utilizing this database, the study updates and refines the key parameters of Henry’s law. The updated Henry’s law has a prediction error of 22.86% at less than 40 MPa, which is an improvement in prediction accuracy compared to before the update. However, the modified Henry’s law suffers from poor calculation accuracy under certain pressure conditions. To further improve the accuracy of solubility prediction, this work also trains a BP (Back Propagation) neural network model based on the database. In addition, MSE (Mean-Square Error) is used as the model evaluation index, and pressure, temperature, compression coefficient, salinity, and fugacity are preferred as input variables, which finally reduces the mean relative error of the model to 16.32%, and the calculation results are more accurate than the modified Henry’s law. In conclusion, this study provides a novel and more accurate method for predicting CH₄ solubility by comparing modified Henry’s law to neural network modeling.