Machine learning and data mining possess excellent abilities of prediction，analysis，decision-making，and calculation and have achieved good results in the field of oil and gas exploration and development. On the basis of summarizing the reservoir prediction methods，this paper analyzes the applicability，advantages and disadvantages of different reservoir prediction methods. It utilizes the machine learning algorithm to predict the rock type，spatial distribution，porosity，permeability，and oil saturation of the reservoir by mining logging and seismic data. This method reveals evident advantages compared with seismic inversion reservoir prediction：first，mining a large amount of information contained in seismic data and multi-attribute fusion can improve the prediction accuracy；second，data-driven instead of experience-driven can simplify the workflow.