{"title":"Production Optimization of a Large Network of Oil Wells with Electrical Submersible Pumps as the Artificial Lift System","authors":"Anisha Roy, Senthilkumar Datchanamoorthy, Nagappa Sharad, Sangeeta Nundy","doi":"10.2523/iptc-22155-ms","DOIUrl":null,"url":null,"abstract":"\n One of the primary tasks of a production engineer at an oil field is to maximize oil production from a field comprising multiple wells, while respecting a multitude of constraints related to operational limitations of components, component reliability, interaction between wells, environmental concerns, and operational costs. This is a multi-objective multi-constraint problem involving multiple physics models that interact with each other. Further, the total number of optimization parameters and constraints grows linearly with the size of the field. This makes the problem computationally intensive for oil fields with hundreds of wells and thus the direct use of a standard optimization algorithm will be inefficient. This paper describes a computationally tractable and scalable approach to solve this problem.","PeriodicalId":10974,"journal":{"name":"Day 2 Tue, February 22, 2022","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, February 22, 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2523/iptc-22155-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
One of the primary tasks of a production engineer at an oil field is to maximize oil production from a field comprising multiple wells, while respecting a multitude of constraints related to operational limitations of components, component reliability, interaction between wells, environmental concerns, and operational costs. This is a multi-objective multi-constraint problem involving multiple physics models that interact with each other. Further, the total number of optimization parameters and constraints grows linearly with the size of the field. This makes the problem computationally intensive for oil fields with hundreds of wells and thus the direct use of a standard optimization algorithm will be inefficient. This paper describes a computationally tractable and scalable approach to solve this problem.