ResGAT:用于岩性识别的残差图注意力网络

Fengda Zhao;Zihan Zhou;Haobing Zhai;Pengwei Zhang;Xianshan Li
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引用次数: 0

摘要

岩性识别对于油气勘探和储层评价至关重要,涉及通过测井数据分析地质样本的物理和化学特征。这一过程需要了解测井参数与岩性之间复杂的非线性关系。最近,图神经网络因其揭示样本间隐藏关系的能力而备受瞩目,从而提高了岩性识别能力。然而,测井数据的不平衡分布往往会导致测井图中不正确的类间连接,从而使特征聚合出现偏差,降低预测精度。为解决这一问题,本文介绍了残差图注意网络(ResGAT),它根据图关系将测井数据的残差信息整合到图网络中,添加残差连接以减轻类间边缘的影响,并增强原始信息的权重。为了真实评估该模型的实际效果,我们分别在中国大庆油田和美国堪萨斯油田的完全孤立井组中进行了跨井预测。与传统的 GAT 和 GCN 模型相比,我们提出的方法实现了更高的识别准确率,并显著提高了对少数类别的预测准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ResGAT: A Residual Graph Attention Network for Lithology Identification
Lithology identification is crucial for oil and gas exploration and reservoir evaluation, involving the analysis of physical and chemical characteristics of geological samples through well-logging data. This process requires understanding the complex nonlinear relationships between logging parameters and lithology. Recently, graph neural networks have gained prominence for their ability to uncover hidden relationships among samples, enhancing lithology identification. However, the imbalanced distribution of logging data often leads to incorrect interclass connections in logging graphs, which can skew feature aggregation and reduce prediction accuracy. To address this issue, this letter introduces the residual graph attention network (ResGAT), which integrates the residual information of well-logging data into the graph network based on graph relationships, adds residual connections to mitigate the impact of interclass edges, and enhances the weight of original information. To authentically assess the model’s practical effectiveness, we, respectively, conducted cross-well predictions in completely isolated well sets in oil fields in Daqing, China, and Kansas, USA. Compared to conventional GAT and GCN models, our proposed method achieves higher identification accuracy and significantly improves prediction accuracy for minority classes.
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