A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir
There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work...
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MDPI AG
2023-01-01
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Online Access: | https://www.mdpi.com/1996-1073/16/1/465 |
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author | Haojie Shang Lihua Cheng Jixin Huang Lixin Wang Yanshu Yin |
author_facet | Haojie Shang Lihua Cheng Jixin Huang Lixin Wang Yanshu Yin |
author_sort | Haojie Shang |
collection | DOAJ |
description | There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the results are different according to different geologists because of the subjective judgment criterion. An efficient and objective method is important to solve the above problem. In this paper, the deep learning image-recognition algorithm is used to automatically and intelligently recognize the facies type from the core image. Through a series of high-reliability preprocessing operations, such as cropping, segmentation, rotation transformation, and noise removal of the original core image, that have been manually identified, the key feature information in the images is extracted based on the ResNet50 convolutional neural network. On the dataset of about 200 core images from 13 facies, an intelligent identification system of facies from core images is constructed, which realizes automatic facies identification from core images. Comparing this method with traditional convolutional neural networks and support vector machines (SVM), the results show that the recognition accuracy of this model is as high as 91.12%, which is higher than the other two models. It is also shown that for a relatively special dataset, such as core images, it is necessary to rely on their global features in order to classify them, and, with a large similarity between some of the categories, it is extremely difficult to classify them. The selection of a suitable neural network model can have a great impact on the accuracy of recognition results. Then, the recognized facies are input as hard data to construct the three-dimensional facies model, which reveals the complex heterogeneity and distribution of the subsurface reservoir for further exploration and development. |
first_indexed | 2024-03-11T10:02:02Z |
format | Article |
id | doaj.art-79c27935030644b18221e9898f202833 |
institution | Directory Open Access Journal |
issn | 1996-1073 |
language | English |
last_indexed | 2024-03-11T10:02:02Z |
publishDate | 2023-01-01 |
publisher | MDPI AG |
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series | Energies |
spelling | doaj.art-79c27935030644b18221e9898f2028332023-11-16T15:19:32ZengMDPI AGEnergies1996-10732023-01-0116146510.3390/en16010465A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands ReservoirHaojie Shang0Lihua Cheng1Jixin Huang2Lixin Wang3Yanshu Yin4School of Geosciences, Yangtze University, 111 University Road, Wuhan 430100, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaSchool of Geosciences, Yangtze University, 111 University Road, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, 111 University Road, Wuhan 430100, ChinaThere is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the results are different according to different geologists because of the subjective judgment criterion. An efficient and objective method is important to solve the above problem. In this paper, the deep learning image-recognition algorithm is used to automatically and intelligently recognize the facies type from the core image. Through a series of high-reliability preprocessing operations, such as cropping, segmentation, rotation transformation, and noise removal of the original core image, that have been manually identified, the key feature information in the images is extracted based on the ResNet50 convolutional neural network. On the dataset of about 200 core images from 13 facies, an intelligent identification system of facies from core images is constructed, which realizes automatic facies identification from core images. Comparing this method with traditional convolutional neural networks and support vector machines (SVM), the results show that the recognition accuracy of this model is as high as 91.12%, which is higher than the other two models. It is also shown that for a relatively special dataset, such as core images, it is necessary to rely on their global features in order to classify them, and, with a large similarity between some of the categories, it is extremely difficult to classify them. The selection of a suitable neural network model can have a great impact on the accuracy of recognition results. Then, the recognized facies are input as hard data to construct the three-dimensional facies model, which reveals the complex heterogeneity and distribution of the subsurface reservoir for further exploration and development.https://www.mdpi.com/1996-1073/16/1/465deep learningcore image facies recognitionMackay River oil sandsCanadasparse datasetsResNet50 convolutional neural network |
spellingShingle | Haojie Shang Lihua Cheng Jixin Huang Lixin Wang Yanshu Yin A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir Energies deep learning core image facies recognition Mackay River oil sands Canada sparse datasets ResNet50 convolutional neural network |
title | A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir |
title_full | A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir |
title_fullStr | A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir |
title_full_unstemmed | A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir |
title_short | A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir |
title_sort | deep learning method for facies recognition from core images and its application a case study of mackay river oil sands reservoir |
topic | deep learning core image facies recognition Mackay River oil sands Canada sparse datasets ResNet50 convolutional neural network |
url | https://www.mdpi.com/1996-1073/16/1/465 |
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