In Journal of contaminant hydrology
Gases that invade during deep-water oil and gas drilling may be concealed due to the gas dissolution effect, leading to increased well control risks. Accurate and rapid prediction of carbon dioxide and methane dissolution is of great significance for the prediction and control of wellbore pressure during gas invasion. In this study, 316 sets of carbon dioxide solubility data at 288.15 to 423.15 K and 0.1 to 100 MPa, and 266 sets of methane solubility data at 275.15 to 444.3 K and 0.1 to 68 MPa were used to train a machine learning algorithm. The machine learning prediction method for gas solubility was established with a support vector regression machine and a particle swarm optimisation algorithm. The kernel function and disciplinary parameters of the support vector regression machine were optimised using the experimental dataset. The solubility of CO2 and CH4 in water was measured using a gas solubility measurement device. The experimental and model analysis showed that the solubility of CO2 varied in different phase states. At a given pressure, the solubility of CO2 was highest in the liquid state, followed by the supercritical state, and then the gaseous state. The average absolute relative deviation percentages between the calculated values of the CO2 and CH4 solubility models and the experimental values were 2.57 and 8.20, respectively. The machine learning method is consistent with the high-precision Duan thermodynamic model for predicting the solubility of CO2 and CH4 in water and can be used to predict the gas solubility in deep water and deep oil and gas drilling.
Sun Baojiang, He Haikang, Sun Xiaohui, Li Xuefeng, Wang Zhiyuan
Carbon dioxide, Machine learning, Methane, Solubility, Thermodynamics