Actual Well Performance Identification and Production Efficiency Enhancement and Sustainability in a Brown Field

Amna Yaaqob Khamis Salem Aladsani, Afra Hamad Alghafli, Sultan Hamdan Al Kaabi, K. Mcneilly, M. M. Akhtar, Deepak Tripathi, Hamda Alkuwaiti, Sandeep Soni, Jose Isambertt
{"title":"Actual Well Performance Identification and Production Efficiency Enhancement and Sustainability in a Brown Field","authors":"Amna Yaaqob Khamis Salem Aladsani, Afra Hamad Alghafli, Sultan Hamdan Al Kaabi, K. Mcneilly, M. M. Akhtar, Deepak Tripathi, Hamda Alkuwaiti, Sandeep Soni, Jose Isambertt","doi":"10.2118/197383-ms","DOIUrl":null,"url":null,"abstract":"\n This paper discusses a production efficiency improvement (PEI) case study using an Integrated Asset Model (IAM) in a super-giant brown field consisting of more than a thousand well strings producing from multi-layered reservoir with different properties. This paper discusses various scenarios that were considered to carry out production efficiency improvement and system bottleneck identification using IAM model integrated within digital framework consisting of automated workflows and advanced data integration.\n IAM solution was implemented in a super-giant brown field to help users to carry out complete system-analysis to assist in delivering production-mandates, identifying sustainability and removing potential bottlenecks for improvements.\n This solution incorporates integration of validated well and network models within a digital-layer, in which various analytical-processes and workflows are automated and integrated with multiple corporate-data-sources. This centralized production-optimization based collaborative-platform enables user to carry out various scenarios while taking into account different operating constraints. Validated and calibrated well and network models were integrated within these workflows, updating them on daily basis, thereby providing representative well and network performance parameters.\n This paper discusses several case studies that were carried out utilizing an integrated asset model, thereby achieving fundamental business objective of production efficiency improvement. For this purpose, full field network models consisting of more than a thousand calibrated well strings were analyzed within a digital IAM framework.\n Various what-if scenarios were adapted to conceptualize an engineering approach in which various reservoir, well and facility level guidelines were incorporated for identifying true potentials of the system. This holistic approach provided users the capability to carry out a detailed analysis to achieve various key production objectives such as reducing production deferrals, compensating production shortfalls, identifying total system capacity and thereby enhancing production efficiency.\n Key challenges and recommendations for improving production efficiency and establishing standardized well potential determination methodology were also highlighted from the case study. Lastly, identification of the true production limits of the reservoir, wells, and the surface network were made possible which is fundamental to the delivery of the long term field development plan.\n Identifying true capacity at the well and field level is a challenging task in a field with more than ten development area with completely different fluid properties and production capacities. A standardized IAM solution approach made this estimation possible. This approach also helped in minimizing potential production deferment thereby leading to cost optimization of total system.","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":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 3 Wed, November 13, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/197383-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

This paper discusses a production efficiency improvement (PEI) case study using an Integrated Asset Model (IAM) in a super-giant brown field consisting of more than a thousand well strings producing from multi-layered reservoir with different properties. This paper discusses various scenarios that were considered to carry out production efficiency improvement and system bottleneck identification using IAM model integrated within digital framework consisting of automated workflows and advanced data integration. IAM solution was implemented in a super-giant brown field to help users to carry out complete system-analysis to assist in delivering production-mandates, identifying sustainability and removing potential bottlenecks for improvements. This solution incorporates integration of validated well and network models within a digital-layer, in which various analytical-processes and workflows are automated and integrated with multiple corporate-data-sources. This centralized production-optimization based collaborative-platform enables user to carry out various scenarios while taking into account different operating constraints. Validated and calibrated well and network models were integrated within these workflows, updating them on daily basis, thereby providing representative well and network performance parameters. This paper discusses several case studies that were carried out utilizing an integrated asset model, thereby achieving fundamental business objective of production efficiency improvement. For this purpose, full field network models consisting of more than a thousand calibrated well strings were analyzed within a digital IAM framework. Various what-if scenarios were adapted to conceptualize an engineering approach in which various reservoir, well and facility level guidelines were incorporated for identifying true potentials of the system. This holistic approach provided users the capability to carry out a detailed analysis to achieve various key production objectives such as reducing production deferrals, compensating production shortfalls, identifying total system capacity and thereby enhancing production efficiency. Key challenges and recommendations for improving production efficiency and establishing standardized well potential determination methodology were also highlighted from the case study. Lastly, identification of the true production limits of the reservoir, wells, and the surface network were made possible which is fundamental to the delivery of the long term field development plan. Identifying true capacity at the well and field level is a challenging task in a field with more than ten development area with completely different fluid properties and production capacities. A standardized IAM solution approach made this estimation possible. This approach also helped in minimizing potential production deferment thereby leading to cost optimization of total system.
棕地实际井情识别、生产效率提高及可持续性
本文讨论了一个利用综合资产模型(IAM)提高生产效率(PEI)的案例研究,该油田由1000多口井串组成,产自具有不同性质的多层油藏。本文讨论了使用集成在由自动化工作流和高级数据集成组成的数字框架内的IAM模型进行生产效率改进和系统瓶颈识别的各种场景。IAM解决方案在一个超大型棕地实施,帮助用户进行完整的系统分析,以协助交付生产任务,确定可持续性并消除潜在的改进瓶颈。该解决方案在数字层中集成了经过验证的井和网络模型,其中各种分析过程和工作流程都是自动化的,并与多个公司数据源集成在一起。这种基于集中生产优化的协作平台使用户能够在考虑不同操作约束的情况下执行各种场景。经过验证和校准的井和网络模型集成在这些工作流程中,每天更新,从而提供具有代表性的井和网络性能参数。本文讨论了利用集成资产模型进行的几个案例研究,从而实现了提高生产效率的基本业务目标。为此,在数字IAM框架内分析了由1000多个校准井串组成的全油田网络模型。为了确定系统的真实潜力,我们采用了各种假设情景,将各种油藏、井和设施级别的指导方针结合起来,形成了一种工程方法。这种整体方法为用户提供了进行详细分析的能力,以实现各种关键的生产目标,如减少生产延迟、补偿生产不足、确定总系统能力,从而提高生产效率。案例分析还强调了提高生产效率和建立标准化井潜力确定方法的主要挑战和建议。最后,确定油藏、油井和地面网络的真实生产极限成为可能,这对交付长期油田开发计划至关重要。在一个拥有10多个开发区域、流体性质和生产能力完全不同的油田中,确定井和油田的真实产能是一项具有挑战性的任务。标准化的IAM解决方案方法使这种估计成为可能。这种方法还有助于最大限度地减少潜在的生产延迟,从而实现整个系统的成本优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信