陈雁,李祉呈,程超,等. FLU-net:用于表征页岩储层微观孔隙的深度全卷积网络[J]. 海洋地质前沿,2021,37(8):34-43. DOI: 10.16028/j.1009-2722.2019.196
    引用本文: 陈雁,李祉呈,程超,等. FLU-net:用于表征页岩储层微观孔隙的深度全卷积网络[J]. 海洋地质前沿,2021,37(8):34-43. DOI: 10.16028/j.1009-2722.2019.196
    CHEN Yan, LI Zhicheng, CHENG Chao, JIAO Shixiang, JIANG Yuqiang, SONG Min, WANG Zhanlei. FLU-NET: A DEEP FULLY CONVOLUTIONAL NEURAL NETWORK FOR SHALE RESERVOIR MICRO-PORE CHARACTERIZATION[J]. Marine Geology Frontiers, 2021, 37(8): 34-43. DOI: 10.16028/j.1009-2722.2019.196
    Citation: CHEN Yan, LI Zhicheng, CHENG Chao, JIAO Shixiang, JIANG Yuqiang, SONG Min, WANG Zhanlei. FLU-NET: A DEEP FULLY CONVOLUTIONAL NEURAL NETWORK FOR SHALE RESERVOIR MICRO-PORE CHARACTERIZATION[J]. Marine Geology Frontiers, 2021, 37(8): 34-43. DOI: 10.16028/j.1009-2722.2019.196

    FLU-net:用于表征页岩储层微观孔隙的深度全卷积网络

    FLU-NET: A DEEP FULLY CONVOLUTIONAL NEURAL NETWORK FOR SHALE RESERVOIR MICRO-PORE CHARACTERIZATION

    • 摘要: 页岩孔隙是页岩气储集的主要空间,孔隙的形状、大小、连通性与发育程度很大程度上决定了页岩储集层的储集性能,因此,页岩气开采首先需要对其孔隙有充分的认识。基于阈值分割法获取页岩孔隙结构参数是目前页岩微观结构表征的一种重要手段,但是受扫描电镜图像灰度分布差异的影响,该方法需要逐一修改图像的最佳分割阈值以达到最好的孔隙分割效果,且阈值分割方法无法直接划分孔隙类别,这给后续的页岩微观结构定量表征带来了麻烦。为了实现页岩孔隙的智能识别和分类,笔者设计基于像素级语义分割的深度全卷积神经网络FLU-net,对页岩孔隙识别并分类为有机孔、无机孔(粒内孔、粒间孔)及裂缝,并结合孔隙尺度分类统计方法,分析不同类型孔隙发育数量、孔径大小、孔隙度等参数,实现页岩储层微观孔隙结构的自动化定量表征。以重庆渝西区块足201井区和四川盆地威远地区威204井区的页岩扫描电镜图像为研究对象,在对1 600幅页岩扫描电镜图像原始数据进行人工标注并划分数据集后,使用FLU-net进行孔隙识别,结果表明,本方法具有较高的准确率,同时自动化程度和泛化能力均高于传统预测方法。因此,扫描电镜与基于深度学习的语义分割模型结合是定量研究页岩微观结构表征的有效手段。

       

      Abstract: Shale pores are the main space of shale gas reservoir. The shape, size, connectivity and development degree of pores may determine the performance of reservoir to a great extent. A full understanding of shale pores is critical before exploitation. At present, obtaining structure parameters of shale pores based on threshold segmentation method plays an important role in shale microstructure characterization. However, due to the difference in gray distribution of SEM images, this method requires modification of the best segmentation threshold individually to achieve the best pore segmentation effect, and the threshold segmentation method cannot classify the pore directly. It may cause trouble to the subsequent quantitative characterization of shale microstructure. In order to realize the intelligent recognition and classification of shale pores, a Deep Convolution Neural Network FLU-net based on pixel-level semantic segmentation is designed in this paper. The pores of shale are identified and classified into organic pores, inorganic pores (intragranular pores and intergranular pores) and fractures. Combined with the statistical method of pore scale classification, the number of pores, pore size, porosity and other parameters for different types of pores are analyzed, and thus automatic quantitative characterization of micro pore structure of shale reservoir is realized. This paper takes the Scanning Electron Microscope (SEM) images of shale from the Zu-201 well area, Yuxi block in Chongqing and the Wei-204 well area, Weiyuan area in Sichuan Basin as the research objects. After manually labeling and dividing the original dataset of 1600 SEM images of shale, FLU-net is used for pore recognition. The results show that this method not only keeps high accuracy, but also has higher automation and generalization ability than traditional prediction methods. Therefore, the combination of SEM and semantic segmentation model based on Deep Learning is an effective mean for quantitative study of shale microstructure characterization.

       

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