Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China
文献类型:期刊论文
作者 | Xin, Wei4; Tian, Fei1,2,3; Shan, Xiaocai1,2,3; Zhou, Yongjian1,2,3; Rong, Huazhong4; Yang, Changchun1,2,3 |
刊名 | WATER
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出版日期 | 2020-06-01 |
卷号 | 12期号:6页码:18 |
关键词 | karst system machine learning Hilbert-Huang transform waveform cluster seismic interpretation paleo-channels |
DOI | 10.3390/w12061765 |
英文摘要 | As deep carbonate fracture-cavity paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity paleokarst reservoirs. By empirical mode decomposition, the time-frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly. |
WOS关键词 | EMPIRICAL MODE DECOMPOSITION ; TAHE OIL-FIELD ; SYSTEM ; ATTRIBUTES ; NETWORK ; UPLIFT ; AREA |
资助项目 | Chinese National Natural Science Foundation[41504142] ; Chinese National Natural Science Foundation[41502149] ; Chinese National Natural Science Foundation[U1663204] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA14050101] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA14040201] ; Chinese National Major Fundamental Research Developing Project[2017ZX05008-004] ; Fundamental Research Funds for the Central Universities of China[ZY1927] ; China Postdoctoral Foundation[2015M570148] ; Horizontal Cooperation Project[H2020009] |
WOS研究方向 | Water Resources |
语种 | 英语 |
WOS记录号 | WOS:000552492200001 |
出版者 | MDPI |
资助机构 | Chinese National Natural Science Foundation ; Chinese National Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Fundamental Research Funds for the Central Universities of China ; Fundamental Research Funds for the Central Universities of China ; China Postdoctoral Foundation ; China Postdoctoral Foundation ; Horizontal Cooperation Project ; Horizontal Cooperation Project ; Chinese National Natural Science Foundation ; Chinese National Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Fundamental Research Funds for the Central Universities of China ; Fundamental Research Funds for the Central Universities of China ; China Postdoctoral Foundation ; China Postdoctoral Foundation ; Horizontal Cooperation Project ; Horizontal Cooperation Project ; Chinese National Natural Science Foundation ; Chinese National Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Fundamental Research Funds for the Central Universities of China ; Fundamental Research Funds for the Central Universities of China ; China Postdoctoral Foundation ; China Postdoctoral Foundation ; Horizontal Cooperation Project ; Horizontal Cooperation Project ; Chinese National Natural Science Foundation ; Chinese National Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Fundamental Research Funds for the Central Universities of China ; Fundamental Research Funds for the Central Universities of China ; China Postdoctoral Foundation ; China Postdoctoral Foundation ; Horizontal Cooperation Project ; Horizontal Cooperation Project |
源URL | [http://ir.iggcas.ac.cn/handle/132A11/97584] ![]() |
专题 | 地质与地球物理研究所_中国科学院油气资源研究重点实验室 |
通讯作者 | Xin, Wei |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China 3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China 4.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China |
推荐引用方式 GB/T 7714 | Xin, Wei,Tian, Fei,Shan, Xiaocai,et al. Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China[J]. WATER,2020,12(6):18. |
APA | Xin, Wei,Tian, Fei,Shan, Xiaocai,Zhou, Yongjian,Rong, Huazhong,&Yang, Changchun.(2020).Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China.WATER,12(6),18. |
MLA | Xin, Wei,et al."Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China".WATER 12.6(2020):18. |
入库方式: OAI收割
来源:地质与地球物理研究所
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