Abstract: To enhance the effectivity and safety of CO₂ geological storage, accurate prediction of CO₂ plume distribution and migration in formations has become essential. Therefore, the partial differential equation (PDE) constraints of multiphase flow were embedded into the loss function of the model by using automatic differential technique, and deep neural network models were developed to predict CO₂ plume distributions, with constraints imposed by multiphase flow mechanics, ensuring that the model’s prediction results not only conform to the distribution law of training data samples but also strictly abide by the physical law of fluid seepage described by the PDE. To validate the model’s effectiveness, two PINN models were constructed using a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. These were applied in a practical case study on CO₂ storage within a depleted oil reservoir. The results show that compared with pure data-driven models, the PINNs-based models demonstrate superior prediction accuracy. The findings of this research provide technical support for the design and implementation of CO₂ geological storage projects while offering a theoretical foundation for the practical application of this technology.