{"title":"地热储层开发的机器学习模拟优化:现状与展望","authors":"M. Rajabi, Mingjie Chen","doi":"10.46690/ager.2022.06.01","DOIUrl":null,"url":null,"abstract":": In geothermal reservoir management, combined simulation-optimization is a practical approach to achieve the optimal well placement and operation that maximizes energy recovery and reservoir longevity. The use of machine learning models is often essential to make simulation-optimization computational feasible. Tools from machine learning can be used to construct data-driven and often physics-free approximations of the numerical model response, with computational times often several orders of magnitude smaller than those required by reservoir numerical models. In this short perspective, we explain the background and current status of machine learning based combined simulation-optimization in geothermal reservoir management, and discuss several key issues that will likely form future directions","PeriodicalId":36335,"journal":{"name":"Advances in Geo-Energy Research","volume":" ","pages":""},"PeriodicalIF":9.0000,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Simulation-optimization with machine learning for geothermal reservoir recovery: Current status and future prospects\",\"authors\":\"M. Rajabi, Mingjie Chen\",\"doi\":\"10.46690/ager.2022.06.01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": In geothermal reservoir management, combined simulation-optimization is a practical approach to achieve the optimal well placement and operation that maximizes energy recovery and reservoir longevity. The use of machine learning models is often essential to make simulation-optimization computational feasible. Tools from machine learning can be used to construct data-driven and often physics-free approximations of the numerical model response, with computational times often several orders of magnitude smaller than those required by reservoir numerical models. In this short perspective, we explain the background and current status of machine learning based combined simulation-optimization in geothermal reservoir management, and discuss several key issues that will likely form future directions\",\"PeriodicalId\":36335,\"journal\":{\"name\":\"Advances in Geo-Energy Research\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":9.0000,\"publicationDate\":\"2022-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Geo-Energy Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46690/ager.2022.06.01\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Geo-Energy Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46690/ager.2022.06.01","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Simulation-optimization with machine learning for geothermal reservoir recovery: Current status and future prospects
: In geothermal reservoir management, combined simulation-optimization is a practical approach to achieve the optimal well placement and operation that maximizes energy recovery and reservoir longevity. The use of machine learning models is often essential to make simulation-optimization computational feasible. Tools from machine learning can be used to construct data-driven and often physics-free approximations of the numerical model response, with computational times often several orders of magnitude smaller than those required by reservoir numerical models. In this short perspective, we explain the background and current status of machine learning based combined simulation-optimization in geothermal reservoir management, and discuss several key issues that will likely form future directions
Advances in Geo-Energy Researchnatural geo-energy (oil, gas, coal geothermal, and gas hydrate)-Geotechnical Engineering and Engineering Geology
CiteScore
12.30
自引率
8.50%
发文量
63
审稿时长
2~3 weeks
期刊介绍:
Advances in Geo-Energy Research is an interdisciplinary and international periodical committed to fostering interaction and multidisciplinary collaboration among scientific communities worldwide, spanning both industry and academia. Our journal serves as a platform for researchers actively engaged in the diverse fields of geo-energy systems, providing an academic medium for the exchange of knowledge and ideas. Join us in advancing the frontiers of geo-energy research through collaboration and shared expertise.