Extreme Learning Machine (ELM) is an easytouse and effective learning algorithm of singlehidden layer feedforward neural networks (SLFNs). The classical learning algorithm in neural networks, e.g.back propagation, requires to set several userdefined parameters and may produce the local minimum. However, the extreme learning machine only requires to set the number of hidden neurons and the activation function. It does not need to adjust  the input weights and hidden layer biases during the implementation of the algorithm, and it produces only one optimal solution. Therefore, ELM has the advantages of fast learning speed and good generalization performance. In this paper, ELM is introduced in predicting reservoir permeability, and by comparing with SVM, we analyse its feasibility and advantages in reservoir permeability prediction. The experimental results show that ELM has similar accuracy compared to SVR, but it has obvious advantages in parameter selection and learning speed.