Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China
Fengcheng Formation in the Mabei Slope of Junggar Basin has low porosity and permeability. However, fractures are well developed, representing an effective storage space for shale oil. Core and experimental data show that the shale oil reservoir of Fengcheng Formation positively correlates with oil...
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| Format: | Article |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1114389/full |
| _version_ | 1828059271722434560 |
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| author | Gang Chen Hongyan Qi Jianglong Yu Wei Li Chenggang Xian Minghui Lu Yong Song Junjun Wu |
| author_facet | Gang Chen Hongyan Qi Jianglong Yu Wei Li Chenggang Xian Minghui Lu Yong Song Junjun Wu |
| author_sort | Gang Chen |
| collection | DOAJ |
| description | Fengcheng Formation in the Mabei Slope of Junggar Basin has low porosity and permeability. However, fractures are well developed, representing an effective storage space for shale oil. Core and experimental data show that the shale oil reservoir of Fengcheng Formation positively correlates with oil content and fractures. And the fracture density has a good quantitatively positive correlation with crude oil production from the production data. Fengcheng Formation has been significantly enriched and accumulated with shale oil due to fractures serving as reservoirs and seepage channels. Therefore, quantitative prediction of fractures is the key to finding high production areas of shale oil in the Fengcheng Formation. The purpose of this study is to extract the seismic attributes that are sensitive to shale oil reservoir fractures. These attributes include curvature, deep learning fracture detection, maximum likelihood, eigenvalue coherence, and variance cube. Furthermore, a seismic multi-attribute fracture density prediction model is trained at the well point using a feedforward neural network method, and the spatial distribution of fracture density is predicted. The results show that the predicted fracture density is consistent with the formation micro imaging logs in the area. Simultaneously, combined with the understanding of the quantitative relationship between fracture density and shale oil production, quantitative prediction results of fracture density could provide the basis for determining the distribution and optimal location of high-quality shale oil wells in the study area. This study will serve as a benchmark for identifying fractures in shale oil reservoirs worldwide. |
| first_indexed | 2024-04-10T21:40:34Z |
| format | Article |
| id | doaj.art-9ace960f1dcc4e7e9edbd2bbbdf5d4d4 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T21:40:34Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Earth Science |
| spelling | doaj.art-9ace960f1dcc4e7e9edbd2bbbdf5d4d42023-01-19T06:14:12ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011110.3389/feart.2023.11143891114389Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, ChinaGang Chen0Hongyan Qi1Jianglong Yu2Wei Li3Chenggang Xian4Minghui Lu5Yong Song6Junjun Wu7PetroChina Xinjiang Oilfield Company, Karamay, ChinaPetroChina Xinjiang Oilfield Company, Karamay, ChinaPetroChina Xinjiang Oilfield Company, Karamay, ChinaPetroChina Xinjiang Oilfield Company, Karamay, ChinaUnconventional Oil and Gas Science and Technology Research Institute, China University of Petroleum, Beijing, ChinaExploration and Development Research Institution, PetroChina Xinjiang Oilfield Company, Beijing, ChinaPetroChina Xinjiang Oilfield Company, Karamay, ChinaPetroChina Xinjiang Oilfield Company, Karamay, ChinaFengcheng Formation in the Mabei Slope of Junggar Basin has low porosity and permeability. However, fractures are well developed, representing an effective storage space for shale oil. Core and experimental data show that the shale oil reservoir of Fengcheng Formation positively correlates with oil content and fractures. And the fracture density has a good quantitatively positive correlation with crude oil production from the production data. Fengcheng Formation has been significantly enriched and accumulated with shale oil due to fractures serving as reservoirs and seepage channels. Therefore, quantitative prediction of fractures is the key to finding high production areas of shale oil in the Fengcheng Formation. The purpose of this study is to extract the seismic attributes that are sensitive to shale oil reservoir fractures. These attributes include curvature, deep learning fracture detection, maximum likelihood, eigenvalue coherence, and variance cube. Furthermore, a seismic multi-attribute fracture density prediction model is trained at the well point using a feedforward neural network method, and the spatial distribution of fracture density is predicted. The results show that the predicted fracture density is consistent with the formation micro imaging logs in the area. Simultaneously, combined with the understanding of the quantitative relationship between fracture density and shale oil production, quantitative prediction results of fracture density could provide the basis for determining the distribution and optimal location of high-quality shale oil wells in the study area. This study will serve as a benchmark for identifying fractures in shale oil reservoirs worldwide.https://www.frontiersin.org/articles/10.3389/feart.2023.1114389/fullshale oil reservoirsfracture density predictionseismic multi-attributeneural networklow porosity and permeability |
| spellingShingle | Gang Chen Hongyan Qi Jianglong Yu Wei Li Chenggang Xian Minghui Lu Yong Song Junjun Wu Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China Frontiers in Earth Science shale oil reservoirs fracture density prediction seismic multi-attribute neural network low porosity and permeability |
| title | Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China |
| title_full | Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China |
| title_fullStr | Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China |
| title_full_unstemmed | Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China |
| title_short | Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China |
| title_sort | application of a multi layer feedforward neural network to predict fracture density in shale oil junggar basin china |
| topic | shale oil reservoirs fracture density prediction seismic multi-attribute neural network low porosity and permeability |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1114389/full |
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