Abstract: The annotation of diagenetic facies samples is a crucial step to ensure the accuracy of intelligent diagenetic facies recognition. In response to the problems of large sample demand and low accuracy in the automatic annotation technology of diagenetic facies samples, this paper proposes an automatic annotation method - AP-GCN, which combines Affinity Propagation Clustering and Graph Convolutional Neural Network. This method fully integrates the advantages of Affinity Propagation Clustering in capturing complex correlation relationships and the ability of Graph Convolutional Neural Network in mining spatial distribution features. The Fuyu oil layer in the Zhouliu block of the Sanzhao depression in the Songliao Basin is selected as the target area to achieve the automatic annotation of diagenetic facies samples. Firstly, the diagenetic facies types are summarized and the logging curve data is preprocessed, with a small number of labels annotated, thereby constructing an automatic annotation dataset, laying the foundation for the subsequent automatic annotation process. Secondly, the graph structure is constructed by using Affinity Propagation Clustering to establish the correlation between the depth nodes of the logging curves. Then, the node features are aggregated through the graph convolutional layer to achieve rapid and accurate annotation of diagenetic facies. Finally, a comparative experiment is designed to verify the effectiveness of the proposed method. The experimental results show that the Precision of AP-GCN method for various diagenetic facies annotations is above 86%, the Recall is above 90%, and the F1 score is above 88%. The accuracy of automatic annotation of diagenetic facies samples is 90.6%, which proves the effectiveness and practicality of this method and provides a new solution for the automatic annotation of diagenetic facies samples.

摘要： 成岩相样本标注是保证成岩相智能识别准确的关键环节。针对成岩相样本自动标注技术存在着样本需求量大和准确率不高的问题，本文提出了一种融合亲和传播聚类和图卷积神经网络相结合的自动标注方法——AP-GCN，该方法充分融合了亲和传播聚类捕捉复杂关联关系的优势和图卷积神经网络挖掘空间分布特征的能力，选取松辽盆地三肇凹陷州六区块扶余油层为靶区，实现成岩相样本的自动标注。首先，归纳成岩相类型并预处理测井曲线数据，进行少量标签标注，由此构建自动标注数据集，为后续自动标注过程奠定了基础；其次，利用亲和传播聚类构造图结构，建立测井曲线深度节点之间的关联关系；然后，通过图卷积层聚合节点特征，实现成岩相的快速准确标注。最后，设计对比实验验证所提方法有效性。实验结果显示，AP-GCN方法对各类成岩相标注Precision在86%以上，Recall在90%以上，F1分数在88%以上，成岩相样本自动标注准确率在90.6%，证明了该方法的有效性和实用性，为成岩相样本自动标注提供了新的解决思路。