Reservoir permeability is an important factor affecting reservoir productivity. In order to solve the problem of poor prediction accuracy of conventional permeability logging models in low-permeability sandstone reservoirs with poor pore connectivity

a scheme combined with deep belief network（DBN） algorithm is proposed. First

the gray correlation method is used for the correlation analysis of logging curve

and according to the correlation ranking

the characteristic sensitive curves is sorted. Then

the optimization by supervised learning is combined with the contrastive divergence for the data mining to establish the prediction model of permeability. Compared to the previous BP neural network

DBN model improves the local optimization

and enhances the training efficiency and prediction accuracy. The average relative error of the prediction model is 9.1 %

which is about 20 % lower than that of the conventional permeability model. Based on the actual data processing applications and the error analysis

it is found that this method can effectively improve the prediction accuracy of permeability for the low permeability reservoirs.