A quantitative interpretation method of gas kick driven by physics-informed neural network

To address the challenges associated with predicting wellbore fluid flow behavior and gas kick rates in deep, complex formations following gas kick events, this study develops a quantitative interpretation method of gas kick driven by physics-informed neural network (PINN). The proposed method integrates a physical model of gas–liquid two-phase flow in the wellbore into the neural network by formulating it as a loss function, leveraging annulus temperature and pressure data obtained from downhole dual measurement tools. The feasibility and effectiveness of this method are evaluated through comparative analysis. The result indicates that during gas kick occurrences, this method achieves mean relative errors of 8.49% and 9.07% for the predicted gas volume fraction and apparent gas phase velocity between the dual measurement points, respectively, and 3.76% for the bottomhole gas kick rate, without the need for mesh discretization or predefined initial conditions, demonstrating strong applicability in field scenarios. Compared to the unscented Kalman filter (UKF) and genetic algorithm (GA), this method exhibits higher prediction accuracy and stability due to its global optimization capability, overcoming the divergence issues encountered by UKF and GA during point-wise recursive predictions under noisy pressure data conditions. Integrating this method with downhole dual measurement tools can provide valuable guidance for blowout risk assessment, well-control method selection, and well-killing parameter design after a gas kick.