Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics
After many years of exploitation in the petroleum field, most of the oil fields are in advanced stages of development, with a strong non-homogeneity of the reservoir, more residual oil, and low recovery efficiency. Therefore, research on various methods has been carried out by scholars to improve th...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2241 |
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| author | Jie Wei Shaohua Li |
| author_facet | Jie Wei Shaohua Li |
| author_sort | Jie Wei |
| collection | DOAJ |
| description | After many years of exploitation in the petroleum field, most of the oil fields are in advanced stages of development, with a strong non-homogeneity of the reservoir, more residual oil, and low recovery efficiency. Therefore, research on various methods has been carried out by scholars to improve the rate of recovery and to understand the distribution pattern of residual oil in reservoirs. Among the whole clastic reservoirs, fluvial reservoirs occupy a large proportion, so fluvial reservoirs will be the priority for future reservoir research in China. The key to the fine characterization of fluvial-phase reservoirs is to able to reproduce the continuous curvature of the channel, and one important parameter is the width of the channel. The width of the channel sand body is one of the key factors in designing well programs, and accurately identifying the channel boundary is the key to identifying a single channel. Traditional research methods cannot accurately characterize the continuous bending and oscillating morphology of underwater diversion channels, and it is not easy to quantitatively characterize the spatial structure. Therefore, in this paper, a deep learning method is applied to quantitatively identify the width of a single channel within an underwater diversion channel at the delta front edge. Based on the sedimentary background of the block and modern depositional studies, we established candidate models for underwater diversion channels with channel widths of 100, 130, 160, 190, 220, and 250 m based on target simulation and human–computer interactions. The results show that when the width of the underwater diversion channel is 160 m, it has the highest matching rate with the conditional data and corresponds to the actual situation. Therefore, it can be determined that it is the common width of underwater diversion channel in the study area. And it is shown that the method can accurately identify the width of underwater diversion channels, and the results provide a basis for reservoir fine characterization studies. |
| first_indexed | 2024-04-24T18:36:11Z |
| format | Article |
| id | doaj.art-f2bedf14789245df816da725b4353f86 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:36:11Z |
| publishDate | 2024-03-01 |
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| series | Applied Sciences |
| spelling | doaj.art-f2bedf14789245df816da725b4353f862024-03-27T13:19:04ZengMDPI AGApplied Sciences2076-34172024-03-01146224110.3390/app14062241Application of Convolutional Neural Network in Quantifying Reservoir Channel CharacteristicsJie Wei0Shaohua Li1College of Geosciences, Yangtze University, Wuhan 430100, ChinaCollege of Geosciences, Yangtze University, Wuhan 430100, ChinaAfter many years of exploitation in the petroleum field, most of the oil fields are in advanced stages of development, with a strong non-homogeneity of the reservoir, more residual oil, and low recovery efficiency. Therefore, research on various methods has been carried out by scholars to improve the rate of recovery and to understand the distribution pattern of residual oil in reservoirs. Among the whole clastic reservoirs, fluvial reservoirs occupy a large proportion, so fluvial reservoirs will be the priority for future reservoir research in China. The key to the fine characterization of fluvial-phase reservoirs is to able to reproduce the continuous curvature of the channel, and one important parameter is the width of the channel. The width of the channel sand body is one of the key factors in designing well programs, and accurately identifying the channel boundary is the key to identifying a single channel. Traditional research methods cannot accurately characterize the continuous bending and oscillating morphology of underwater diversion channels, and it is not easy to quantitatively characterize the spatial structure. Therefore, in this paper, a deep learning method is applied to quantitatively identify the width of a single channel within an underwater diversion channel at the delta front edge. Based on the sedimentary background of the block and modern depositional studies, we established candidate models for underwater diversion channels with channel widths of 100, 130, 160, 190, 220, and 250 m based on target simulation and human–computer interactions. The results show that when the width of the underwater diversion channel is 160 m, it has the highest matching rate with the conditional data and corresponds to the actual situation. Therefore, it can be determined that it is the common width of underwater diversion channel in the study area. And it is shown that the method can accurately identify the width of underwater diversion channels, and the results provide a basis for reservoir fine characterization studies.https://www.mdpi.com/2076-3417/14/6/2241neural networkunderwater distributary channelchannel widthquantitative evaluation |
| spellingShingle | Jie Wei Shaohua Li Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics Applied Sciences neural network underwater distributary channel channel width quantitative evaluation |
| title | Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics |
| title_full | Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics |
| title_fullStr | Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics |
| title_full_unstemmed | Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics |
| title_short | Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics |
| title_sort | application of convolutional neural network in quantifying reservoir channel characteristics |
| topic | neural network underwater distributary channel channel width quantitative evaluation |
| url | https://www.mdpi.com/2076-3417/14/6/2241 |
| work_keys_str_mv | AT jiewei applicationofconvolutionalneuralnetworkinquantifyingreservoirchannelcharacteristics AT shaohuali applicationofconvolutionalneuralnetworkinquantifyingreservoirchannelcharacteristics |