IGGCAS OpenIR  > 油气资源研究院重点实验室
Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China
Xin, Wei1; Tian, Fei2,3,4; Shan, Xiaocai2,3,4; Zhou, Yongjian2,3,4; Rong, Huazhong1; Yang, Changchun2,3,4
2020-06-01
Source PublicationWATER
Volume12Issue:6Pages:18
AbstractAs 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.
Keywordkarst system machine learning Hilbert-Huang transform waveform cluster seismic interpretation paleo-channels
DOI10.3390/w12061765
Funding OrganizationChinese 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
WOS KeywordEMPIRICAL MODE DECOMPOSITION ; TAHE OIL-FIELD ; SYSTEM ; ATTRIBUTES ; NETWORK ; UPLIFT ; AREA
Language英语
Funding ProjectChinese 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]
Funding OrganizationChinese 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
WOS Research AreaWater Resources
WOS SubjectWater Resources
WOS IDWOS:000552492200001
PublisherMDPI
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/97584
Collection油气资源研究院重点实验室
Corresponding AuthorXin, Wei
Affiliation1.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
3.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
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.
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