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

doi:10.19657/j.geoscience.1000-8527.2021.003

Abstract

Abstract:

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.

Key words: cavity automatic recognition, convolution neural network, digital outcrop, deep learning

CLC Number:

TE122.2

WANG Qing, ZENG Qihong, ZHANG Youyan, SHAO Yanlin, WEI Wei, DENG Fan. Automatic Extraction of Outcrop Cavity Based on Multi-scale Regional Convolution Neural Network[J]. Geoscience, 2021, 35(04): 1147-1154.

Figures/Tables 11

References 25

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方法	精准率	召回率	准确率	F1-Score
OSTU	0.045 8	0.934 8	0.825 8	0.087 4
分水岭	0.067 0	0.513 7	0.931 9	0.118 5
BP神经网络	0.210 5	0.693 8	0.974 1	0.322 9
支持向量机	0.266 5	0.607 6	0.981 6	0.370 5
Mask-RCNN	0.587 2	0.537 1	0.989 2	0.561 0
我们的模型	0.700 1	0.749 1	0.994 9	0.723 8

方法	精准率	召回率	准确率	F1-Score
OSTU	0.045 8	0.934 8	0.825 8	0.087 4
分水岭	0.067 0	0.513 7	0.931 9	0.118 5
BP神经网络	0.210 5	0.693 8	0.974 1	0.322 9
支持向量机	0.266 5	0.607 6	0.981 6	0.370 5
Mask-RCNN	0.587 2	0.537 1	0.989 2	0.561 0
我们的模型	0.700 1	0.749 1	0.994 9	0.723 8

方法	精准率	召回率	准确率	F1-Score
OSTU	0.044 1	0.913 2	0.828 4	0.084 2
分水岭	0.055 9	0.434 2	0.931 8	0.099 1
BP神经网络	0.253 2	0.479 7	0.983 3	0.331 4
支持向量机	0.291 3	0.437 8	0.985 9	0.349 8
Mask-RCNN	0.426 9	0.387 2	0.986 8	0.406 1
我们的模型	0.607 5	0.631 7	0.993 3	0.619 4

方法	精准率	召回率	准确率	F1-Score
OSTU	0.044 1	0.913 2	0.828 4	0.084 2
分水岭	0.055 9	0.434 2	0.931 8	0.099 1
BP神经网络	0.253 2	0.479 7	0.983 3	0.331 4
支持向量机	0.291 3	0.437 8	0.985 9	0.349 8
Mask-RCNN	0.426 9	0.387 2	0.986 8	0.406 1
我们的模型	0.607 5	0.631 7	0.993 3	0.619 4

主要参数	实验区域一				实验区域二
主要参数	模型	人工	准确度/%		模型		人工		准确度/%
洞数量	28	25	88.00	43		39		89.74
面孔率/%	0.95	0.89	93.26	0.88		0.86		97.67
洞面积均值 /mm²	224.10	238.22	94.07	131.69		140.79		93.54