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Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin
  • 2024-01-08
元数据
名   称 Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin
科技资源标识 CSTR:11738.14.NCDC.XDA14.PP5666.2024
DOI https://doi.org/10.1016/j.marpetgeo.2020.104720
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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.
学科分类
170.20
关键词
Paleokarst reservoirs Electrofacies PCA K-means LDA
作者 郑文浩,田飞,底青云
数据量 16.6 MiB
论文类型： journal
论文网址： https://www.sciencedirect.com/science/article/pii/S0264817220305031
期刊名称： Marine and Petroleum Geology
出版时间： 2021-01-01
引用和标注
数据引用
郑文浩,田飞,底青云. Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin. 国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn), 2024. https://cstr.cn/CSTR:11738.14.NCDC.XDA14.PP5666.2024.
郑文浩,田飞,底青云. Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin. 国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn), 2024. https://www.doi.org/https://doi.org/10.1016/j.marpetgeo.2020.104720.
许可协议
知识共享许可协议   本作品采用 知识共享署名 4.0 国际许可协议进行许可。
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