Integrated Network Modelling for More Robust Production Prediction in Challenging Subsea Deepwater Development

Colinus Lajim Sayung, Mei Fen Foo, N. Hamza, M. K. Sahrudin, A. Khalid
{"title":"Integrated Network Modelling for More Robust Production Prediction in Challenging Subsea Deepwater Development","authors":"Colinus Lajim Sayung, Mei Fen Foo, N. Hamza, M. K. Sahrudin, A. Khalid","doi":"10.2118/210643-ms","DOIUrl":null,"url":null,"abstract":"\n L-B is a clustered deepwater development comprising two greenfields currently approaching execution stage. The development concept is subsea umbilical, risers and flowlines (SURF) with dedicated Floating Production Storage and Offloading (FPSO) in L field and a long subsea tieback (20km) for B field. Due to this, production assurance is a major risk particularly for B field during production. To enable a holistic simulation of the production and injection system, an integrated network model (INM) is developed. This paper presents the systematic and integrated approach in developing the INM for L-B cluster, the calibration processes, and the resulting field modifications undertaken as an outcome of the model.\n The INM comprises dynamic reservoir model, well model and network model coupled using an integrator software. Robustness of each standalone model were assured through stringent construction and reviews by respective disciplines. Multiple collaborative forums participated by cross-function members were held to integrate the models. Next, a custom algorithm and method were developed to address specific field controls such as staggered voidage replacement ratio and skin growth over time. Once the models were compatible, multiple scenarios identified from Concept Identification Workshop were evaluated with INM. The results were then integrated into fiscal evaluations and ultimately facilitated decision-making for L-B project.\n Thorough utilization of the completed INM models generated vital data for future cluster production forecast of L and B fields:\n The in-situ FPSO operating pressure was accurately simulated using INM resulting in a dynamically responsive production profile, instead of sole dependence on reservoir model which uses a static pressure set up. INM was also used to identify and mitigate potential bottleneck along production system. Preliminary artificial lift options of Electrical Submersible Pump (ESP), Downhole Gaslift (DHG), Subsea Multiphase Pump (MPP) and Riser based Gaslift (RBGL) were analyzed and selectively narrowed down using INM. Outcomes of the analysis were favorable to MPP and RBGL which were then incorporated in the Concept Select scenarios. Ten scenarios with permutations on recovery method, onset of pressure booster installation, and artificial lift requirement were analyzed and decisively selected using results from INM. Study of new technology such as subsea separator was also concluded to be inapplicable in the field via INM evaluation. Finalized temperature modeling was used to cater for flow assurance constraints such as minimum Flowing Tubing Head Temperature (FTHT) requirement and generated inflow information to be incorporated into specialized Flow Assurance (FA) software.\n This paper will highlight the benefits of a comprehensive integrated network model covering end-to-end operations to mitigate flow assurance risk prior to field start-up. This model will also be readily utilized during the crucial production stage for calibration with actual field data to generate reliable prediction. The long-term application of INM will give greater assurance of production attainability in the L-B clustered development.","PeriodicalId":151564,"journal":{"name":"Day 1 Mon, October 17, 2022","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 1 Mon, October 17, 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/210643-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

L-B is a clustered deepwater development comprising two greenfields currently approaching execution stage. The development concept is subsea umbilical, risers and flowlines (SURF) with dedicated Floating Production Storage and Offloading (FPSO) in L field and a long subsea tieback (20km) for B field. Due to this, production assurance is a major risk particularly for B field during production. To enable a holistic simulation of the production and injection system, an integrated network model (INM) is developed. This paper presents the systematic and integrated approach in developing the INM for L-B cluster, the calibration processes, and the resulting field modifications undertaken as an outcome of the model. The INM comprises dynamic reservoir model, well model and network model coupled using an integrator software. Robustness of each standalone model were assured through stringent construction and reviews by respective disciplines. Multiple collaborative forums participated by cross-function members were held to integrate the models. Next, a custom algorithm and method were developed to address specific field controls such as staggered voidage replacement ratio and skin growth over time. Once the models were compatible, multiple scenarios identified from Concept Identification Workshop were evaluated with INM. The results were then integrated into fiscal evaluations and ultimately facilitated decision-making for L-B project. Thorough utilization of the completed INM models generated vital data for future cluster production forecast of L and B fields: The in-situ FPSO operating pressure was accurately simulated using INM resulting in a dynamically responsive production profile, instead of sole dependence on reservoir model which uses a static pressure set up. INM was also used to identify and mitigate potential bottleneck along production system. Preliminary artificial lift options of Electrical Submersible Pump (ESP), Downhole Gaslift (DHG), Subsea Multiphase Pump (MPP) and Riser based Gaslift (RBGL) were analyzed and selectively narrowed down using INM. Outcomes of the analysis were favorable to MPP and RBGL which were then incorporated in the Concept Select scenarios. Ten scenarios with permutations on recovery method, onset of pressure booster installation, and artificial lift requirement were analyzed and decisively selected using results from INM. Study of new technology such as subsea separator was also concluded to be inapplicable in the field via INM evaluation. Finalized temperature modeling was used to cater for flow assurance constraints such as minimum Flowing Tubing Head Temperature (FTHT) requirement and generated inflow information to be incorporated into specialized Flow Assurance (FA) software. This paper will highlight the benefits of a comprehensive integrated network model covering end-to-end operations to mitigate flow assurance risk prior to field start-up. This model will also be readily utilized during the crucial production stage for calibration with actual field data to generate reliable prediction. The long-term application of INM will give greater assurance of production attainability in the L-B clustered development.
在具有挑战性的水下深水开发中,集成网络建模可实现更稳健的产量预测
L-B是一个集群式深水开发项目,包括两个绿地,目前正接近执行阶段。开发理念是海底脐带、立管和流动管线(SURF),在L油田配备专用的浮式生产储存和卸载(FPSO),在B油田采用长水下回接(20km)。因此,在生产过程中,生产保证是一个主要的风险,特别是对于B油田。为了实现对生产和注入系统的整体模拟,开发了一个集成网络模型(INM)。本文介绍了为L-B集群开发INM的系统和综合方法,校准过程,以及作为模型结果进行的最终现场修改。该模型包括动态储层模型、井模型和网络模型,通过集成软件进行耦合。每个独立模型的鲁棒性都通过严格的构建和各自学科的审查来保证。举办了多个由跨职能成员参与的协作论坛,以整合模型。接下来,开发了一种自定义算法和方法来解决特定的现场控制问题,如交错空隙替换率和皮肤随时间的生长。一旦模型兼容,从概念识别研讨会确定的多个场景将使用INM进行评估。然后将结果整合到财政评估中,最终促进了L-B项目的决策。完整的INM模型为L和B油田的未来集群生产预测提供了重要数据:使用INM精确模拟了FPSO的现场操作压力,从而获得了动态响应的生产剖面,而不是仅仅依赖于使用静压设置的油藏模型。INM还用于识别和缓解生产系统中的潜在瓶颈。对电潜泵(ESP)、井下气举(DHG)、水下多相泵(MPP)和立管气举(RBGL)的初步人工举升方案进行了分析,并使用INM选择性地缩小了范围。分析结果有利于MPP和RBGL,然后将其纳入概念选择方案。利用INM的结果,分析了采油方式、启动增压装置和人工举升需求等10种不同的方案,并进行了果断选择。通过INM评估,海底分离器等新技术的研究也被认为不适用于现场。最终的温度建模用于满足流动保证约束,如最低流动油管头温度(FTHT)要求,并生成流入信息,将其纳入专门的流动保证(FA)软件中。本文将重点介绍覆盖端到端作业的综合集成网络模型的优势,以降低油田启动前的流动保障风险。在关键的生产阶段,该模型也可以很容易地用于与实际现场数据进行校准,以产生可靠的预测。INM的长期应用将为L-B集群开发的生产可达性提供更大的保证。
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