{"title":"基于机器学习算法的基岩棕色油田开发分析","authors":"M. Naugolnov, A. Antropov, J. Arsić","doi":"10.3997/2214-4609.202156022","DOIUrl":null,"url":null,"abstract":"Summary The purpose of the work is a new approach to the development analysis of brown oil field, that is located in basement rocks. Analysis is done for the tasks of the future implementation of the pressure maintenance system with the usage of advanced analytics tools and machine learning algorithms. The solution is based on the integration of well performance data and field studies, as well as on the study of the mutual influence of wells as a factor characterizing the fracture throughput, wells clasterisation and production forecast.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Using of Machine Learning Algorithms for Development Analysis of a Brown oil Field Located in The Basement Rocks\",\"authors\":\"M. Naugolnov, A. Antropov, J. Arsić\",\"doi\":\"10.3997/2214-4609.202156022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary The purpose of the work is a new approach to the development analysis of brown oil field, that is located in basement rocks. Analysis is done for the tasks of the future implementation of the pressure maintenance system with the usage of advanced analytics tools and machine learning algorithms. The solution is based on the integration of well performance data and field studies, as well as on the study of the mutual influence of wells as a factor characterizing the fracture throughput, wells clasterisation and production forecast.\",\"PeriodicalId\":266953,\"journal\":{\"name\":\"Data Science in Oil and Gas 2021\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science in Oil and Gas 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.202156022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science in Oil and Gas 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202156022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using of Machine Learning Algorithms for Development Analysis of a Brown oil Field Located in The Basement Rocks
Summary The purpose of the work is a new approach to the development analysis of brown oil field, that is located in basement rocks. Analysis is done for the tasks of the future implementation of the pressure maintenance system with the usage of advanced analytics tools and machine learning algorithms. The solution is based on the integration of well performance data and field studies, as well as on the study of the mutual influence of wells as a factor characterizing the fracture throughput, wells clasterisation and production forecast.