{"title":"智能井系统","authors":"Edgar Camargo, Egner Aceros, J. Aguilar","doi":"10.1109/APCASE.2015.10","DOIUrl":null,"url":null,"abstract":"In this paper is presented an Intelligent Well Systems for the Industrial Production of Oil. Such scheme is tested for gas lift (GL) oil wells. The proposal is based on the production assessment, in the utilization of the process variables (specifically, the bottom-well and surfaces pressures), and the operational scenarios detection (in the case study, the production of the oil well), with the objective of optimizing the producing performance of the well. The proposal combines intelligent techniques (Fuzzy Classification Systems) and Mass Energy Balance. Our approach allows determining and controlling the oil or gas flow that a well can produce, taking into account the completion geometry and the reservoir potential, as well as the criteria related to the well's performance curves of oil and gas.","PeriodicalId":235698,"journal":{"name":"2015 Asia-Pacific Conference on Computer Aided System Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Intelligent Well Systems\",\"authors\":\"Edgar Camargo, Egner Aceros, J. Aguilar\",\"doi\":\"10.1109/APCASE.2015.10\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper is presented an Intelligent Well Systems for the Industrial Production of Oil. Such scheme is tested for gas lift (GL) oil wells. The proposal is based on the production assessment, in the utilization of the process variables (specifically, the bottom-well and surfaces pressures), and the operational scenarios detection (in the case study, the production of the oil well), with the objective of optimizing the producing performance of the well. The proposal combines intelligent techniques (Fuzzy Classification Systems) and Mass Energy Balance. Our approach allows determining and controlling the oil or gas flow that a well can produce, taking into account the completion geometry and the reservoir potential, as well as the criteria related to the well's performance curves of oil and gas.\",\"PeriodicalId\":235698,\"journal\":{\"name\":\"2015 Asia-Pacific Conference on Computer Aided System Engineering\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Asia-Pacific Conference on Computer Aided System Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APCASE.2015.10\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Asia-Pacific Conference on Computer Aided System Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APCASE.2015.10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper is presented an Intelligent Well Systems for the Industrial Production of Oil. Such scheme is tested for gas lift (GL) oil wells. The proposal is based on the production assessment, in the utilization of the process variables (specifically, the bottom-well and surfaces pressures), and the operational scenarios detection (in the case study, the production of the oil well), with the objective of optimizing the producing performance of the well. The proposal combines intelligent techniques (Fuzzy Classification Systems) and Mass Energy Balance. Our approach allows determining and controlling the oil or gas flow that a well can produce, taking into account the completion geometry and the reservoir potential, as well as the criteria related to the well's performance curves of oil and gas.