With the deepening of oilfield development, the remained oil which shows some effects on oil displacement efficiency is formed in the reservoir pore-throat. At present, the study on the remained oil is almost performed by doing microscopic displacement experiments with the visual glass etching model. Research on remained oil shape in the model can provide important reference for secondary oil recovery and tertiary oil recovery of oilfield. In this work, the geometric feature parameters of remained oil are taken as the input of neural network for classification. Through the training and testing of the BP(Back-Propagation) neural network, good recognition rates can be achieved, which enables fast and accurate classification and identification for remained oil shape.%随着油田开发的不断深入，储集层孔喉内形成剩余油，这些剩余油在一定程度上影响驱油效率。目前，对剩余油的研究主要是通过可视化的玻璃刻蚀模型进行微观动态驱替实验。对于模型中的剩余油形态进行研究分析，可以为油田的二次采油以及三次采油提供重要参考依据。本文使用剩余油形态的几何特征参数作为 BP 神经网络的输入对其进行分类识别。通过对该BP神经网络的训练测试，其具有良好的识别率，能达到快速准确分类识别剩余油形态的目的。
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