Erratum: Effects of Gas Dissolution on Gas Migration during Gas Invasion in Drilling.

[This corrects the article DOI: 10.1021/acsomega.2c04097.].

Effects of Gas Dissolution on Gas Migration during Gas Invasion in Drilling.

Sour gas reservoirs (including CO2 and H2S) are vulnerable to gas invasion when drilling into reservoir sections. The high solubility of the invaded gas in drilling fluid makes the gas invasion monitoring "hidden" and "sudden" for later expansion, and the blowout risk increases. Accurate prediction of gas dissolution is highly significant for monitoring gas invasion. In this study, the gas-liquid flow control equations considering gas dissolution were established. Focusing on the gas dissolution effect, a solubility experiment for CO2 and CH4 in an aqueous solution was performed using a phase equilibrium device. The experimental and simulation results revealed that the addition of CO2 can significantly increase gas dissolution, and the presence of salts decreases it. For solubility prediction of pure CH4 and CO2, the fugacity-activity solubility model, calculated using the Peng-Robinson equation of state, was more accurate than the Soave-Redlich-Kwong equation of state. The Soave-Redlich-Kwong equation of state has higher accuracy for the CO2 and CH4 gas mixture. If the gas dissolution effect is considered for wellbore gas-liquid flow, the time required for the mud pit gain to reach the early warning value increases. When the contents of CO2 and H2S in intrusive gases are higher, the time for mud pit gain change monitored on the ground increases, the concealment increases, and the risk of blowout increases.

Prediction method of solubility of carbon dioxide and methane during gas invasion in deep-water drilling.

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.