{"title":"机器学习预测注水和注汽共同驱动的成熟陆上油田产油量","authors":"L. Kubota, Danilo Reinert","doi":"10.2118/196152-ms","DOIUrl":null,"url":null,"abstract":"\n In this paper, we tackle an old problem – production forecast - using techniques that are relatively new to the reservoir engineer toolbox. The problem at hand consists of forecasting oil production in a mature onshore field simultaneously driven by water and steam injection. However, instead of turning to traditional methods, we deploy machine-learning algorithms which will feed on a plethora of historical data to extract hidden patterns and underlying relationships with a view to forecasting oil rate. No geological model and/or numerical reservoir simulators will be needed, only 3 sets of time-series: injection history, production history and number of producers. Two Machine-Learning algorithms are used: linear-regression and recurrent neural networks.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"87 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Machine Learning Forecasts Oil Rate in Mature Onshore Field Jointly Driven by Water and Steam Injection\",\"authors\":\"L. Kubota, Danilo Reinert\",\"doi\":\"10.2118/196152-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n In this paper, we tackle an old problem – production forecast - using techniques that are relatively new to the reservoir engineer toolbox. The problem at hand consists of forecasting oil production in a mature onshore field simultaneously driven by water and steam injection. However, instead of turning to traditional methods, we deploy machine-learning algorithms which will feed on a plethora of historical data to extract hidden patterns and underlying relationships with a view to forecasting oil rate. No geological model and/or numerical reservoir simulators will be needed, only 3 sets of time-series: injection history, production history and number of producers. Two Machine-Learning algorithms are used: linear-regression and recurrent neural networks.\",\"PeriodicalId\":10909,\"journal\":{\"name\":\"Day 2 Tue, October 01, 2019\",\"volume\":\"87 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 2 Tue, October 01, 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/196152-ms\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, October 01, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/196152-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine Learning Forecasts Oil Rate in Mature Onshore Field Jointly Driven by Water and Steam Injection
In this paper, we tackle an old problem – production forecast - using techniques that are relatively new to the reservoir engineer toolbox. The problem at hand consists of forecasting oil production in a mature onshore field simultaneously driven by water and steam injection. However, instead of turning to traditional methods, we deploy machine-learning algorithms which will feed on a plethora of historical data to extract hidden patterns and underlying relationships with a view to forecasting oil rate. No geological model and/or numerical reservoir simulators will be needed, only 3 sets of time-series: injection history, production history and number of producers. Two Machine-Learning algorithms are used: linear-regression and recurrent neural networks.