Manual fracture identification methods based on cores and image logging pseudo‐pictures are limited by the expense and the amount of data. In this paper, we propose an integrated workflow, which takes the fracture identification as an end‐to‐end project, to combine the boundary detection and the deep learning classification to recognize fractured zones with accurate locations and reasonable thickness. We first apply the discrete wavelet transform algorithm and a boundary detection method named changing point detection to enhance the fracture sensibility of acoustic logs and segment the whole logging interval into non‐overlapping subsections by estimating boundaries. The deep neural network based auto‐encoders and the convolutional neural network classifier are then implemented to extract the hidden information from logs and categorize the subsections as the fractured or non‐fractured zones. To validate the feasibility of this workflow, we apply it to the logging data from a real well. Compare with the benchmarks provided by the support vector machine , random forest and Adaboost model, the one‐dimensional well profile predicted by the proposed changing point detection‐deep learning classifier is more consistent with the manual identification result.

中文翻译：

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深度学习辅助测井反演识别裂缝

基于岩心和图像记录伪图片的人工裂缝识别方法受到费用和数据量的限制。在本文中，我们提出了一个集成的工作流程，将裂缝识别作为一个端到端的项目，以结合边界检测和深度学习分类来识别具有精确位置和合理厚度的裂缝区域。我们首先应用离散小波变换算法和边界检测方法（称为变化点检测）来增强声波测井的裂缝敏感性，并通过估计边界将整个测井间隔划分为不重叠的子部分。然后实现基于深度神经网络的自动编码器和卷积神经网络分类器，以从日志中提取隐藏信息，并将子部分分类为破裂或未破裂区域。为了验证此工作流程的可行性，我们将其应用于一口实际井的测井数据。与支持向量机，随机森林和Adaboost模型提供的基准相比，所提出的变化点检测-深度学习分类器预测的一维井剖面与人工识别结果更加一致。