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  2. 地质与地球物理研究所
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  4. 深部资源勘探装备研发
Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin

文献类型：期刊论文

作者Zheng, Wenhao3,4,5; Tian, Fei3,4,5; Di, Qingyun3,4,5; Xin, Wei1; Cheng, Fuqi2; Shan, Xiaocai3,4,5
刊名MARINE AND PETROLEUM GEOLOGY
出版日期2021
卷号123页码:13
关键词Paleokarst reservoirs Electrofacies PCA K-means LDA
ISSN号0264-8172
DOI10.1016/j.marpetgeo.2020.104720
英文摘要The paleokarst system is one of the main carbonate reservoirs, which can form important super-large oil fields. There are many typical paleokarst reservoirs in the Tarim Basin Ordovician strata, mainly composed of caves, vugs, and fractures. Due to the deep burial depth and strong heterogeneity, qualitative identifying the different scale fracture-vuggy reservoirs from the tight limestone around the wellbore is a real challenge in the industrial community. In this paper, machine learning methods were used to classify electrofacies. Firstly, core samples and electrical imaging logging of the paleokarst reservoirs are observed in detail and a core-electrical imaging chart was established. Secondly, conventional logging data was optimized and preprocessed for data mining, using Principal Component Analysis (PCA) algorithm and K-means algorithm. High-resolution electrical imaging logging was chosen as a constraint to recognize electrofacies, and an electrofacies-lithology database was established. Thirdly, based on the electrofacies-lithology database, Linear Discriminant Analysis (LDA) algorithm was used to build an electrofacies prediction model, which can automatically identify the electrofacies in carbonate strata, with a coincidence rate of 92.2%. Finally, the model was used to quantitatively recognize paleokarst reservoirs and their distributions. The electrofacies machine learning workflow proposed in this paper could be used in Tarim Basin and other similar paleokarst reservoirs, which can improve exploration efficiency and save economic cost.
WOS关键词MARCELLUS SHALE LITHOFACIES ; TAHE OIL-FIELD ; NEURAL-NETWORK ; BOREHOLE IMAGE ; PREDICTION ; FACIES ; LOGS ; ORIGIN ; SYSTEM
资助项目Chinese National key research and development program[2019YFA0708301] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA14050101] ; Chinese National Natural Science Foundation of China[41502149] ; Chinese National Natural Science Foundation of China[U1663204] ; Chinese National Major Fundamental Research Developing Project[2017ZX05008004] ; China National Petroleum Corporation (CNPC)[2019B-04] ; China National Petroleum Corporation (CNPC)[2018A-0102] ; China National Petroleum Corporation (CNPC)[H2020009]
WOS研究方向Geology
语种英语
WOS记录号WOS:000599497300002
出版者ELSEVIER SCI LTD
资助机构Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Chinese National key research and development program ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Natural Science Foundation of China ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; Chinese National Major Fundamental Research Developing Project ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC) ; China National Petroleum Corporation (CNPC)
源URL[http://ir.iggcas.ac.cn/handle/132A11/99950]  
专题地质与地球物理研究所_深部资源勘探装备研发
通讯作者Tian, Fei
作者单位1.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
2.China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
5.Chinese Acad Sci, Inst Geol & Geophys, Ctr Frontier Technol & Equipment Dev Deep Resourc, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Zheng, Wenhao,Tian, Fei,Di, Qingyun,et al. Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin[J]. MARINE AND PETROLEUM GEOLOGY,2021,123:13.
APA Zheng, Wenhao,Tian, Fei,Di, Qingyun,Xin, Wei,Cheng, Fuqi,&Shan, Xiaocai.(2021).Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin.MARINE AND PETROLEUM GEOLOGY,123,13.
MLA Zheng, Wenhao,et al."Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin".MARINE AND PETROLEUM GEOLOGY 123(2021):13.

入库方式： OAI收割

来源：地质与地球物理研究所

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