ISSN 1004-4140
CN 11-3017/P
陈康, 狄贵东, 张佳佳, 周游, 吴尧, 张广智. 基于改进U-Net卷积神经网络的储层预测[J]. CT理论与应用研究, 2021, 30(4): 403-416. DOI: 10.15953/j.1004-4140.2021.30.04.01
引用本文: 陈康, 狄贵东, 张佳佳, 周游, 吴尧, 张广智. 基于改进U-Net卷积神经网络的储层预测[J]. CT理论与应用研究, 2021, 30(4): 403-416. DOI: 10.15953/j.1004-4140.2021.30.04.01
CHEN Kang, DI Guidong, ZHANG Jiajia, ZHOU You, WU Yao, ZHANG Guangzhi. Reservoir Prediction Based on Improved U-Net Convolutional Neural Network[J]. CT Theory and Applications, 2021, 30(4): 403-416. DOI: 10.15953/j.1004-4140.2021.30.04.01
Citation: CHEN Kang, DI Guidong, ZHANG Jiajia, ZHOU You, WU Yao, ZHANG Guangzhi. Reservoir Prediction Based on Improved U-Net Convolutional Neural Network[J]. CT Theory and Applications, 2021, 30(4): 403-416. DOI: 10.15953/j.1004-4140.2021.30.04.01

基于改进U-Net卷积神经网络的储层预测

Reservoir Prediction Based on Improved U-Net Convolutional Neural Network

  • 摘要: 传统的U-Net卷积神经网络大多存在深层网络梯度消失的问题。本文在U-Net卷积神经网络中加入残差模块,提出了一种改进U-Net卷积神经网络。残差模块保证了U-Net卷积神经网络在误差反向传播过程中梯度的存在,在一定程度上可以缓解梯度消失的问题。最后将改进U-Net卷积神经网络应用于实际储层预测中,实际数据测试结果表明基于改进U-Net卷积神经网络在岩性识别以及“甜点”预测上均能取得较好的效果。

     

    Abstract: Most of the traditional U-Net convolutional neural networks have the problem that the gradient of the deep network disappears. In this paper, a residual module is added to the U-Net convolutional neural network, and an improved U-Net convolutional neural network is proposed. The residual module guarantees the existence of the gradient of the U-Net convolutional neural network in the process of error back-propagation, which can alleviate the problem of gradient disappearance to a certain extent. Finally, the improved U-Net convolutional neural network is applied to the actual reservoir prediction. The actual data measurement shows that the improved U-Net convolutional neural network can achieve better results in lithology identification and "Sweet Point" prediction.

     

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