基于多尺度区域卷积神经网络的露头孔洞自动提取

碳酸盐岩储层解释与评价的一个重要内容是确定其孔隙空间特征,野外露头是地下储层的真实刻画,对野外露头进行孔洞的自动化提取与其参数定量表征具有重要意义。在深度学习Mask-RCNN模型基础上进行改进,提出一种基于多尺度的区域卷积神经网络孔洞检测新方法,并通过两种方式进行准确度分析,第一种是将该方法的孔洞提取结果与OSTU分割法、分水岭分割法、BP神经网络法、支持向量机法以及Mask-RCNN的孔洞提取结果进行比较,其结果显示该方法有更高的检测准确度;第二种是通过该方法提取的孔洞结果计算洞数量、面孔率和洞面积均值三个孔洞特征参数,以人工提取结果为参照,比较得到洞数量准确度在88%以上,面孔率准确度在93%以上,洞面积均值准确度在93%以上。最后将提出的方法应用于峨边先锋灯二段的数字露头剖面孔洞自动识别,并分层计算孔洞参数,定量分析其分布特征,为该露头碳酸盐岩储层评价提供了依据。

Automatic Extraction of Outcrop Cavity Based on Multi-scale Regional Convolution Neural Network

Determination of the pore space characteristics is important for carbonate reservoir interpretation and evaluation. Field outcrop can reflect the geology of the underground reservoir, and thus can be used to identify the cavity automatically and characterize their parameters. In this study, through enhancing the deep-learning model Mask-RCNN, a new cavity detection method based on a multi-scale regional convolution neural network is proposed, with its accuracy being verified by two methods: (1) By comparing the cavity extraction results of this method with those of OSTU, watershed, BP neural network, support vector machine, and Mask-RCNN, it is shown that the method has higher detection accuracy; (2) By calculating the three cavity characteristic parameters of cavity number, surface porosity, and the average cavity area through the cavity results extracted by the method, and by comparing the results of manual extraction, it is shown that the accuracy for the cavity number, surface porosity, and average cavity area is over 88%, 93%, and 93%, respectively. Consequently, the proposed method is applied to the automatic cavity identification in the digital outcrop profile of Dengying Formation (2^nd Member) in Xianfeng, Ebian. We calculated the cavity parameters in the layers, and quantitatively analyze their distribution characteristics, in order to provide a carbonate reservoir evaluation basis for this outcrop.