Unbalanced graph isomorphism network for fracture identification by well logs

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ning Ma , Shaoqun Dong , Lexiu Wang , Leting Wang , Xu Yang , Shuo Liu
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引用次数: 0

Abstract

Fracture identification and prediction are of great significance for the production of tight oil and gas reservoirs. The high angles of fractures limit their traceability and reduce drilling intersection, leading to significant data imbalance and making fracture identification an imbalanced classification problem. Lithology and fluid properties can create similar features in fracture samples, often resulting in nonlinear relationships and a non-Euclidean structure. This complexity makes fracture identification a nonlinear process. To address this issue, the unbalanced graph isomorphism network (UGIN) algorithm is introduced. This approach leverages the GIN and incorporates a binary cross-entropy loss function specifically designed for unbalanced samples during fracture identification, aiming to adjust the model’s focus toward minority classes by assigning higher penalties to misclassified fracture samples, thereby improving detection accuracy in imbalanced datasets. The identification process is divided into three stages: First, the sample logging similarity information is integrated into the graph structure using the sequence edge method. Second, node-level information is embedded via the GIN algorithm, and nodes are clustered using K-means to derive the local graph’s embedding representation. Finally, nodes are classified using the model. To test the validation of the UGIN algorithm, a dataset of fractured carbonate reservoirs in A Oilfield, of the Zagros Mountain fold belt is used. The results demonstrate robust generalization on both training and test datasets through the use of cross-validation, achieving an AUC score of 0.938, higher than the baseline model. The classification accuracy on test data reaches 96.7%, with particularly strong performance in identifying fracture samples. To evaluate the impact of different graph construction methods on UGIN’s performance, we compare the K-means clustering method, hierarchical clustering method, the comprehensive connectivity method, the enhanced linkage strategy and the sequence edge method. Results indicate that the sequence edge method performs best, maximizing the retention of depth-related information in logging features and enhancing sample embedding.
利用测井记录识别压裂的非平衡图同构网络
裂缝识别和预测对致密油气藏的生产具有重要意义。裂缝的高角度限制了其可追溯性并减少了钻井交叉,导致数据严重失衡,使裂缝识别成为一个失衡分类问题。岩性和流体性质会在裂缝样本中产生类似的特征,往往会导致非线性关系和非欧几里得结构。这种复杂性使得断裂识别成为一个非线性过程。为解决这一问题,引入了不平衡图同构网络(UGIN)算法。这种方法利用了 GIN,并在断裂识别过程中加入了专为不平衡样本设计的二元交叉熵损失函数,旨在通过对分类错误的断裂样本给予更高的惩罚,调整模型对少数类别的关注,从而提高不平衡数据集的检测精度。识别过程分为三个阶段:首先,使用序列边缘法将样本测井相似性信息整合到图结构中。其次,通过 GIN 算法嵌入节点级信息,并使用 K-means 对节点进行聚类,以得出局部图的嵌入表示。最后,使用模型对节点进行分类。为了测试 UGIN 算法的有效性,我们使用了扎格罗斯山褶皱带 A 油田的碳酸盐岩裂缝储层数据集。结果表明,通过使用交叉验证,UGIN 算法在训练和测试数据集上都具有强大的泛化能力,AUC 得分为 0.938,高于基线模型。测试数据的分类准确率达到 96.7%,在识别断裂样本方面表现尤为突出。为了评估不同图构建方法对 UGIN 性能的影响,我们比较了 K 均值聚类法、层次聚类法、综合连接法、增强链接策略和序列边缘法。结果表明,序列边缘法性能最佳,能最大限度地保留日志特征中与深度相关的信息,并增强样本嵌入。
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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