剩余油的形态分布情况对油田的深度开发有着重大意义. 针对剩余油数据量较少和传统的形态参数分类能力有限等问题, 提取一种基于深度学习的剩余油形态分类方法. 该方法在数据预处理部分, 利用生成对抗网络ACGAN的多类别数据生成特性对剩余油图像进行数据增强; 采用VGG19模型作为主干网络提取传统形态参数无法描述的深层特征, 同时引入SENet注意力机制, 改善模型特征表达能力, 使得最终的分类结果更加精确. 为验证本研究方法的有效性, 将本文方法与传统形态参数和其他深度学习模型的分类方法进行对比, 并通过主观视觉和客观指标进行评估, 结果表明本文方法分类更为精确.
The distribution of remaining oil forms is of great significance for the deep development of oil fields. This study proposes a form classification method of remaining oil based on deep learning to address the problems of scarce remaining oil data and the limited ability of traditional morphological parameter classification. In the data preprocessing stage, the method uses the multi-class data generation characteristics of the generative adversarial network (ACGAN) to enhance the data of the remaining oil image. It employs the VGG19 model as the backbone network to extract deep features that cannot be described by traditional morphological parameters and introduces the SENet attention mechanism to improve the model’s feature expression ability, making the final classification results more accurate. To verify the effectiveness, the proposed method is compared with traditional classification methods based on morphological parameters and other deep learning models, and it is evaluated through subjective visual and objective indicators. The results showed that the proposed method provides a more accurate classification.