| 基于改进U-Net卷积神经网络的储层预测 |
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| 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. |
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| 作者姓名： | 陈康 狄贵东 张佳佳 周游 吴尧 张广智 |
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| 作者单位： | 1. 中国石油西南油气田分公司勘探开发研究院, 成都 610041; |
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| 基金项目： | 国家自然科学基金（42074136；41674130）；中央高校基础研究业务费专项基金（18CX02061A）；中国石油科技创新基金（2016D-5007-0301）；中国石油科学研究与技术开发项目（2017D-3504）。 |
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| 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。
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| 关 键 词： | 卷积神经网络 U-Net 深度学习 岩性识别 |
| 收稿时间： | 2020-12-20 |
Reservoir Prediction Based on Improved U-Net Convolutional Neural Network |
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| CHEN Kang,DI Guidong,ZHANG Jiajia,ZHOU You,WU Yao,ZHANG Guangzhi.Reservoir Prediction Based on Improved U-Net Convolutional Neural Network[J].Computerized Tomography Theory and Applications,2021,30(4):403-416. |
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| Authors: | CHEN Kang DI Guidong ZHANG Jiajia ZHOU You WU Yao ZHANG Guangzhi |
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| Affiliation: | 1. Exploration and Development Research Institute, Petrochina Southwest Oil & Gas field Company, Chengdu 610041, China;2. School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China;3. Key Laboratory of Deep Oil and Gas Geology and Exploration, Ministry of Education, Qingdao 266580, China |
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| 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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