中东蒸汽驱油田优化示范工程

E. Behm, Mohammed Al Asimi, Sara Al Maskari, Wladimir Juna, H. Klie, Duc Le, G. Lutidze, R. Rastegar, A. Reynolds, Vinit Tathed, R. Younis, Yuchen Zhang
{"title":"中东蒸汽驱油田优化示范工程","authors":"E. Behm, Mohammed Al Asimi, Sara Al Maskari, Wladimir Juna, H. Klie, Duc Le, G. Lutidze, R. Rastegar, A. Reynolds, Vinit Tathed, R. Younis, Yuchen Zhang","doi":"10.2118/197751-ms","DOIUrl":null,"url":null,"abstract":"\n Occidental Mukhaizna completed a steamflood field optimization demonstration project involving about 100 Mukhaizna wells from Mid-December 2018 to Mid-March 2019.\n The field demonstration involves a data analytics process that provides recommendations on the best steam injection allocation among wells in order to improve overall steamflood performance. The process uses a low fidelity physics-based proxy model and cloud-based parallel processing. A field optimization engineer history matches and anchors a proxy model to current well and field operating constraints. The engineer completes hundreds of forward runs as part of an optimization algorithm to identify scenarios most likely to help increase value (oil production per steam injected) over the short term in the field, while honoring all producing and injection well operating ranges. The reservoir management team vets the rate change ideas generated and provides their recommendations for changes so the likely best and most practical overall scenario is implemented. The process is refreshed monthly so field performance results are included immediately, and the optimization process is kept evergreen. The field results so far have been encouraging, yielding an increase in oil production that has exceeded expectations.\n This paper will describe the data analytics field optimization process and workflow, present the baseline performance versus field demonstration results, and share lessons learned.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Middle East Steamflood Field Optimization Demonstration Project\",\"authors\":\"E. Behm, Mohammed Al Asimi, Sara Al Maskari, Wladimir Juna, H. Klie, Duc Le, G. Lutidze, R. Rastegar, A. Reynolds, Vinit Tathed, R. Younis, Yuchen Zhang\",\"doi\":\"10.2118/197751-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Occidental Mukhaizna completed a steamflood field optimization demonstration project involving about 100 Mukhaizna wells from Mid-December 2018 to Mid-March 2019.\\n The field demonstration involves a data analytics process that provides recommendations on the best steam injection allocation among wells in order to improve overall steamflood performance. The process uses a low fidelity physics-based proxy model and cloud-based parallel processing. A field optimization engineer history matches and anchors a proxy model to current well and field operating constraints. The engineer completes hundreds of forward runs as part of an optimization algorithm to identify scenarios most likely to help increase value (oil production per steam injected) over the short term in the field, while honoring all producing and injection well operating ranges. The reservoir management team vets the rate change ideas generated and provides their recommendations for changes so the likely best and most practical overall scenario is implemented. The process is refreshed monthly so field performance results are included immediately, and the optimization process is kept evergreen. The field results so far have been encouraging, yielding an increase in oil production that has exceeded expectations.\\n This paper will describe the data analytics field optimization process and workflow, present the baseline performance versus field demonstration results, and share lessons learned.\",\"PeriodicalId\":11091,\"journal\":{\"name\":\"Day 3 Wed, November 13, 2019\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 3 Wed, November 13, 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/197751-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 3 Wed, November 13, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/197751-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

Occidental Mukhaizna于2018年12月中旬至2019年3月中旬完成了一个蒸汽驱油田优化示范项目,涉及约100口Mukhaizna井。现场演示包括数据分析过程,为井间最佳注汽分配提供建议,以提高整体蒸汽驱性能。该过程使用基于物理的低保真代理模型和基于云的并行处理。现场优化工程师的历史将代理模型与当前井和现场的操作限制相匹配并锚定。作为优化算法的一部分,工程师完成了数百次正向下入,以确定最有可能在短期内提高油田价值(每注入蒸汽的产油量)的方案,同时满足所有生产和注水井的作业范围。油藏管理团队对产生的速率变化想法进行审查,并提供他们的变更建议,以便实施可能最好、最实用的总体方案。该过程每月更新一次,因此现场性能结果立即包含在内,并且优化过程保持常青。到目前为止,该油田的结果令人鼓舞,石油产量的增长超出了预期。本文将描述数据分析现场优化过程和工作流程,展示基线性能与现场演示结果,并分享经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Middle East Steamflood Field Optimization Demonstration Project
Occidental Mukhaizna completed a steamflood field optimization demonstration project involving about 100 Mukhaizna wells from Mid-December 2018 to Mid-March 2019. The field demonstration involves a data analytics process that provides recommendations on the best steam injection allocation among wells in order to improve overall steamflood performance. The process uses a low fidelity physics-based proxy model and cloud-based parallel processing. A field optimization engineer history matches and anchors a proxy model to current well and field operating constraints. The engineer completes hundreds of forward runs as part of an optimization algorithm to identify scenarios most likely to help increase value (oil production per steam injected) over the short term in the field, while honoring all producing and injection well operating ranges. The reservoir management team vets the rate change ideas generated and provides their recommendations for changes so the likely best and most practical overall scenario is implemented. The process is refreshed monthly so field performance results are included immediately, and the optimization process is kept evergreen. The field results so far have been encouraging, yielding an increase in oil production that has exceeded expectations. This paper will describe the data analytics field optimization process and workflow, present the baseline performance versus field demonstration results, and share lessons learned.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Treatment of Produced Water with Back Produced ASP Using Analytics to Assess Health Status of DLE Combustion Gas Turbines Design of Economical Polymer and Surfactant-Polymer Processes in High Temperature Carbonated Um Gudair Kuwaiti Field. Enhancement of Routine Data Acquisition in a Giant Offshore Brownfield by Bridging Gaps Identified Through Comprehensive Data Analysis Applying Brain Science to Achieve Organizational Success
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1