{"title":"BP神经网络在油田产量预测中的应用","authors":"Lei Sun, Yange Bi, Guorong Lu","doi":"10.1109/WCSE.2010.101","DOIUrl":null,"url":null,"abstract":"This paper introduces a new Neural Network model which is suitable for oil production prediction with training parameter set. From the comparison between prediction of oil production and real production, the precision of prediction meets the requirements quite well. In addition, this new model offers better self-adaptive ability and can be used in multi-cycle and multi-descending production forecast. In general, BP Neural Network is an ideal mean for oil production prediction.","PeriodicalId":376358,"journal":{"name":"2010 Second World Congress on Software Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Application of BP Neural Network in Oil Field Production Prediction\",\"authors\":\"Lei Sun, Yange Bi, Guorong Lu\",\"doi\":\"10.1109/WCSE.2010.101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a new Neural Network model which is suitable for oil production prediction with training parameter set. From the comparison between prediction of oil production and real production, the precision of prediction meets the requirements quite well. In addition, this new model offers better self-adaptive ability and can be used in multi-cycle and multi-descending production forecast. In general, BP Neural Network is an ideal mean for oil production prediction.\",\"PeriodicalId\":376358,\"journal\":{\"name\":\"2010 Second World Congress on Software Engineering\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second World Congress on Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCSE.2010.101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second World Congress on Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCSE.2010.101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of BP Neural Network in Oil Field Production Prediction
This paper introduces a new Neural Network model which is suitable for oil production prediction with training parameter set. From the comparison between prediction of oil production and real production, the precision of prediction meets the requirements quite well. In addition, this new model offers better self-adaptive ability and can be used in multi-cycle and multi-descending production forecast. In general, BP Neural Network is an ideal mean for oil production prediction.