The complicated geological conditions and the strong horizontal and vertical heterogeneity of the reservoir severe? ly restrict the water injection development efficiency of 22F Well Block in offshore Chengdao Oilfield，Shengli Oil Province.Accurately identifying the water injection situation of layers in a reservoir is an important prerequisite for reservoir management. It also has important guiding significance for the formulation of a reasonable water injection development plan. Therefore，a data-driven method is proposed for water injection profile prediction in this paper. The Extreme Gradient Boosting （XGBoost）algorithm is used to construct a model for making water injection profile prediction，with which the evolution of the water injection profile of each water injection well during the entire development period is predicted using the geological parameters and dynamic production data of the reservoir. As a result，high-quality data can be provided for rational production allocation and injection-production scheme adjustments. The application results in 22F Well Block of offshore Chengdao Oilfield show that the proposed method can accurately inverse and predict the water injection profile with an average relative error of 0.04，a determination coefficient of 0.87，and a root mean squared error of 3.12. Compared with the KH splitting method，the model in this paper yields a predicted value more consistent with the actual water absorption. This demonstrates that the proposed method can better reflect the actual water absorption of the reservoir and lays a solid foundation for fine stratified water injection and intelligent development of oilfields.