Petroleum Reservoir Evaluation and Development >
2021 , Vol. 11 >Issue 4: 577 - 585
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2021.04.014
Prediction of reservoir permeability by deep belief network based on optimized parameters
Received date: 2020-10-29
Online published: 2021-08-19
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.
Jun ZHAO , Tao ZHANG , Shenglin HE , Huanrong ZHANG , Dong HAN , Di TANG . Prediction of reservoir permeability by deep belief network based on optimized parameters[J]. Petroleum Reservoir Evaluation and Development, 2021 , 11(4) : 577 -585 . DOI: 10.13809/j.cnki.cn32-1825/te.2021.04.014
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