Shear-Wave Velocity Prediction by CNN-GRU Fusion Network Based on the Self-Attention Mechanism

Yahua Yang;Junfeng Zhao;Huanfu Du;Xingyao Yin;Tengfei Chen
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Abstract

Elastic parameters such as compressional-wave velocity and shear-wave velocity are essential for characterizing and predicting oil-gas reservoirs. However, the current commonly used shear-wave velocity prediction methods have problems such as weak generalization of empirical formulas and difficulty in obtaining some rock parameters in various rock physics models. We proposed a deep learning network based on the self-attention mechanism to predict shear-wave velocity. First, we need to extract the spatial and temporal features of well logging data using convolutional neural network (CNN) and gated recurrent unit (GRU), respectively. However, the spatial and temporal features exhibit different correlations in the depth direction due to the gradual variation of sedimentary layers. Thus, we fuse the self-attention mechanism with the deep learning network to enhance the network’s sensitivity to crucial spatiotemporal features. Finally, we take the tight sandstone reservoir of Tarim Basin as the research object to estimate shear-wave velocity using CNN, GRU network, and our optimized method. The results show that the CNN-GRU fusion network based on the self-attention mechanism network we proposed is better than the other two networks in the prediction accuracy and generalization degree.
基于自关注机制的CNN-GRU融合网络横波速度预测
压缩波速和横波波速等弹性参数是油气储层表征和预测的重要参数。然而,目前常用的横波速度预测方法存在经验公式泛化性弱、各种岩石物理模型中某些岩石参数难以获得等问题。我们提出了一种基于自注意机制的深度学习网络来预测横波速度。首先,利用卷积神经网络(CNN)和门控递归单元(GRU)分别提取测井数据的时空特征。然而,由于沉积层的逐渐变化,时空特征在深度方向上表现出不同的相关性。因此,我们将自注意机制与深度学习网络融合,以增强网络对关键时空特征的敏感性。最后,以塔里木盆地致密砂岩储层为研究对象,利用CNN、GRU网络及优化方法估算横波速度。结果表明,基于自注意机制网络的CNN-GRU融合网络在预测精度和泛化程度上优于其他两种网络。
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