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

摘要/Abstract

摘要：

储层渗透率是储层产能的一个重要影响因素。针对常规测井渗透率模型在孔隙连通性差的低渗砂岩储层预测精度不高的问题,提出利用深度置信网络算法结合常规测井曲线预测储层渗透率的方法。该方法利用灰色关联法对测井曲线进行了关联度分析,依据相关度排序选取了特征敏感测井曲线,结合深度置信网络的有监督学习调优与对比散度算法进行数据挖掘,建立了渗透率的预测模型。该模型在以往BP神经网络的基础上改善了局部优化的问题,提高了网络模型的训练效率与预测精度。预测模型的平均相对误差为9.1 %,相比常规渗透率模型,降低了20 %左右。通过对实际资料的处理应用,结合误差分析,表明该方法能够有效地提高低渗透储层渗透率的预测精度。

关键词: 渗透率, 测井曲线, 灰色关联分析法, 深度置信网络, 预测

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

中图分类号:

TE31

赵军,张涛,何胜林等. 基于参数优选的储层渗透率深度置信网络模型预测初探[J]. 油气藏评价与开发, 2021, 11(4): 577-585.

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