{"title":"基于BP神经网络的致密砂岩储层产能预测","authors":"Yulei Wang","doi":"10.3968/9476","DOIUrl":null,"url":null,"abstract":"To survey He-8 member tight sand reservoir with low porosity and permeability in Mizhi gas field in Ordos basin, using the conventional well log data, this paper proposes the tight sand reservoir productivity prediction model and classification criterion based on BP neural network, getting quick classification of gas well productivity. We can predict sand reserve quantitatively instead qualitatively with the methods.Applications show that the methods of productivity prediction are effective and practical.","PeriodicalId":313367,"journal":{"name":"Advances in Petroleum Exploration and Development","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Productivity Prediction of Tight Sandstone Reservoir Based on BP Neural Network\",\"authors\":\"Yulei Wang\",\"doi\":\"10.3968/9476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To survey He-8 member tight sand reservoir with low porosity and permeability in Mizhi gas field in Ordos basin, using the conventional well log data, this paper proposes the tight sand reservoir productivity prediction model and classification criterion based on BP neural network, getting quick classification of gas well productivity. We can predict sand reserve quantitatively instead qualitatively with the methods.Applications show that the methods of productivity prediction are effective and practical.\",\"PeriodicalId\":313367,\"journal\":{\"name\":\"Advances in Petroleum Exploration and Development\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Petroleum Exploration and Development\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3968/9476\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Petroleum Exploration and Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3968/9476","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Productivity Prediction of Tight Sandstone Reservoir Based on BP Neural Network
To survey He-8 member tight sand reservoir with low porosity and permeability in Mizhi gas field in Ordos basin, using the conventional well log data, this paper proposes the tight sand reservoir productivity prediction model and classification criterion based on BP neural network, getting quick classification of gas well productivity. We can predict sand reserve quantitatively instead qualitatively with the methods.Applications show that the methods of productivity prediction are effective and practical.