{"title":"主成分分析法在煤储层孔隙度评价中的应用","authors":"Jingang Wu, Guang Zhang","doi":"10.1145/3335656.3335695","DOIUrl":null,"url":null,"abstract":"In this paper, we study the feasibility of applying the principal component analysis (PCA) for porosity evaluation of a coal Reservoir. The geological characteristics of the No. 3 coal reservoir in Qinshui Basin (Shanxi Province, China) were analyzed at first. On this basis, vitrinite reflectance, coal macrolithotype, ash content, macro-fissure density, micro-fissure density, and coal structure were adopted as the index variables for evaluating the porosity. Three principal components were extracted by reducing dimensions of the six primitive variables. Then, a scoring model of the principal components was constructed to calculate the comprehensive scores for porosity of the coal samples. In addition, Qinshui Basin was partitioned into four regions: a (Yangquan), b (Jingfang-Wangzhuang), c (Changcun), and d (Jincheng), which were listed in a descending order as a, c, b, and d with reducing porosities. Explanation of the principal components and geological theoretical analysis revealed that the geotectonic movements and evolutions, and sedimentary environment are main controlling factors for studying the porosity based zoning of the research region. The results prove that it is feasible to apply PCA to evaluate the porosity of coal reservoirs.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of PCA in the Porosity Evaluation of a Coal Reservoir\",\"authors\":\"Jingang Wu, Guang Zhang\",\"doi\":\"10.1145/3335656.3335695\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we study the feasibility of applying the principal component analysis (PCA) for porosity evaluation of a coal Reservoir. The geological characteristics of the No. 3 coal reservoir in Qinshui Basin (Shanxi Province, China) were analyzed at first. On this basis, vitrinite reflectance, coal macrolithotype, ash content, macro-fissure density, micro-fissure density, and coal structure were adopted as the index variables for evaluating the porosity. Three principal components were extracted by reducing dimensions of the six primitive variables. Then, a scoring model of the principal components was constructed to calculate the comprehensive scores for porosity of the coal samples. In addition, Qinshui Basin was partitioned into four regions: a (Yangquan), b (Jingfang-Wangzhuang), c (Changcun), and d (Jincheng), which were listed in a descending order as a, c, b, and d with reducing porosities. Explanation of the principal components and geological theoretical analysis revealed that the geotectonic movements and evolutions, and sedimentary environment are main controlling factors for studying the porosity based zoning of the research region. The results prove that it is feasible to apply PCA to evaluate the porosity of coal reservoirs.\",\"PeriodicalId\":396772,\"journal\":{\"name\":\"Proceedings of the 2019 International Conference on Data Mining and Machine Learning\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 International Conference on Data Mining and Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3335656.3335695\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3335656.3335695","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of PCA in the Porosity Evaluation of a Coal Reservoir
In this paper, we study the feasibility of applying the principal component analysis (PCA) for porosity evaluation of a coal Reservoir. The geological characteristics of the No. 3 coal reservoir in Qinshui Basin (Shanxi Province, China) were analyzed at first. On this basis, vitrinite reflectance, coal macrolithotype, ash content, macro-fissure density, micro-fissure density, and coal structure were adopted as the index variables for evaluating the porosity. Three principal components were extracted by reducing dimensions of the six primitive variables. Then, a scoring model of the principal components was constructed to calculate the comprehensive scores for porosity of the coal samples. In addition, Qinshui Basin was partitioned into four regions: a (Yangquan), b (Jingfang-Wangzhuang), c (Changcun), and d (Jincheng), which were listed in a descending order as a, c, b, and d with reducing porosities. Explanation of the principal components and geological theoretical analysis revealed that the geotectonic movements and evolutions, and sedimentary environment are main controlling factors for studying the porosity based zoning of the research region. The results prove that it is feasible to apply PCA to evaluate the porosity of coal reservoirs.