基于参数优选的储层渗透率深度置信网络模型预测初探

doi:10.13809/j.cnki.cn32-1825/te.2021.04.014

Abstract

Abstract:

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.

Key words: permeability, logging curve, grey relational analysis, deep belief network, prediction

CLC Number:

TE31

Jun ZHAO,Tao ZHANG,Shenglin HE, et al. Prediction of reservoir permeability by deep belief network based on optimized parameters[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 577-585.

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测井类别	关联度	排序
声波时差（AC）	0.874 7	2
密度（DEN）	0.892 5	1
自然伽马（GR）	0.723 0	3
补偿中子（CNL）	0.667 9	4
井径（CAL）	0.507 7	8
自然电位（SP）	0.557 7	7
浅侧向（RILM）	0.656 4	5
深侧向（RILD）	0.648 6	6

井号	深度段（m）	常规孔隙度预测模型（%）	BP神经网络模型（%）	深度置信网络模型（%）
WC1井	3 663.3~3 678.9	32.70	22.30	10.60
WC2井	3 849.7~3 867.7	28.40	18.40	7.20
WC3井	3 987.8~4 006.1	30.30	19.20	8.14
WC4井	3 851~3 855.4	28.10	19.90	9.25
WC5井	3 753.6~3 771.6	31.20	20.10	10.40
平均值		30.14	19.98	9.12

Prediction of reservoir permeability by deep belief network based on optimized parameters

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