二氧化碳项目管理的集成自动化和数据驱动工作流——来自中东智能油田的案例研究

Erismar Rubio, M. Y. Alklih, N. Reddicharla, Abobaker Albelazi, Melike Dilsiz, Mohamed Ali Al-Attar, R. Davila, K. Khan
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引用次数: 0

摘要

自动化和数据驱动模型已被证明在世界各地的几个油田取得了商业成功,并报告了与改进油藏管理相关的技术优势。本文演示了在阿布扎比的一个大型陆上智能油田实施一个集成工作流程,以提高二氧化碳注入项目的性能。自投入使用以来,通过基于智能异常的监测程序,可以对油藏/模式/井的性能进行评估,并支持决策过程,从而实现对油藏管理策略的主动评估。延长每口井的生产可持续性是这项工作的关键支柱,通过实时跟踪产出的二氧化碳含量和腐蚀指标,这项工作变得更加可量化。密集的计算技术任务和来自不同来源的数据聚合;例如井测试和实时生产/注入测量;都集成在一个单一平台的自动工作流中。因此,实时可视化和仪表板也自动生成;以系统和有效的方式整合信息、模型和多学科知识;使工程师能够专注于有问题的井,并及时关注机会的产生。与数值技术和其他决策支持工具相辅相成,智能系统数据驱动模型有助于在更短的时间内获得可靠的短期预测,并有助于在日常运营优化方面做出快速决策。这些仪表板可以测量井/模式的真实性能,以实现作业目标和生产目标。一套完整的KPI有助于识别井的健康状况和潜在风险,从而减轻短期/长期开采的风险,从而在日常基础上获得最佳的油藏能量平衡。如果出现意外的井况行为,仪表板可以提供有关不同井况根本原因的数据见解,从而提出相应的补救措施。通过在生产者周围提供合适的压力支持,采取主动行动,持续评估生产者与注入器的连接性/相互依赖性,改善注入/生产计划,根据监测结果验证/调整流线模型,避免二氧化碳回收,优化数据采集计划,从而节省潜在成本,同时采取预防措施,最大限度地减少油井/设施的腐蚀影响,从而确保二氧化碳混相性。在这项工作中,实施了最佳的油藏管理措施,使油田的采收率增加了12%。应用方法采用集成的自动化和数据驱动建模方法,实时解决二氧化碳注入项目管理挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrated Automation and Data-Driven Workflow for CO2 Project Management – Case Study from a Smart Oil Field in the Middle-East
Automation and data-driven models have been proven to yield commercial success in several oil fields worldwide with reported technical advantages related to improved reservoir management. This paper demonstrates the implementation of an integrated workflow to enhance CO2 injection project performance in a giant onshore smart oil field in Abu Dhabi. Since commissioning, proactive evaluation of the reservoir management strategy is enabled via smart-exception-based surveillance routines that facilitate reservoir/pattern/well performance review and supporting the decision making process. Prolonging the production sustainability of each well is a key pillar of this work, which has been made more quantifiable using live-tracking of the produced CO2 content and corrosion indicators. The intensive computing technical tasks and data aggregation from different sources; such as well testing and real time production/injection measurements; are integrated in an automatic workflow in a single platform. Accordingly, real-time visualizations and dashboards are also generated automatically; to orchestrate information, models and multidisciplinary knowledge in a systematic and efficient manner; allowing engineers to focus on problematic wells and giving attention to opportunity generation in a timely manner. Complemented with numerical techniques and other decision support tools, the intelligent system data-driven model assist to obtain a reliable short-term forecast in a shorter time and help making quick decisions on day-to-day operational optimization aspects. These dashboards have allowed measuring the true well/pattern performance towards operational objectives and production targets. A complete set of KPI's has helped to identify well health-status, potential risks and thus mitigate them for short/long term recovery to obtain an optimum reservoir energy balance in daily bases. In case of unexpected well performance behaviors, the dashboards have provided data insights on the root causes of different well issues and thus remedial actions were proposed accordingly. Maintaining CO2 miscibility is also ensured by having the right pressure support around producers, taking proactive actions from continues evaluation of producer-injector connectivity/interdependency, improving injection/production schedule, validating/tuning streamline model based on surveillance insights, avoiding CO2 recycling, optimizing data acquisition plan with potential cost saving while taking preventive measures to minimize well/facility corrosion impact. In this work, best reservoir management practices have been implemented to create a value of 12% incremental oil recovery from the field. The applied methodology uses an integrated automation and data-driven modeling approach to tackle CO2 injection project management challenges in real-time.
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