采用带注意力机制3D U-Net网络的地质模型参数化技术

A 3D attention U-Net network and its application in geological model parameterization

针对卷积神经网络增强的主成分分析技术(CNN-PCA)这种地质模型参数化技术在油藏复杂地质特征刻画精度和泛化能力方面存在的问题,不使用预训练好的C3D视频动作分析模型来提取三维模型风格特征,而使用新的损失函数并引入一种带注意力机制的3D U-Net网络来补全主成分分析方法(PCA)降维过程中丢失的地质模型细节信息,并以一个复合河道砂体油藏为例进行了应用效果分析。研究表明,与CNN-PCA技术相比,采用带注意力机制的3DU-Net网络能够更好地补全PCA降维过程中丢失的地质模型细节信息,在反映原始地质模型的流动特性方面具有更好的效果,并能改善油藏历史拟合的技术效果。

To solve the problems of convolutional neural network–principal component analysis(CNN-PCA) in fine description and generalization of complex reservoir geological features, a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model, and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study. The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects. The results show that compared with CNN-PCA method, the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction, better reflect the fluid flow features in the original geologic model, and improve history matching results.