BP阿曼的敏捷性——用更少的钱做更多的事

Ghaida Al Farsi, Angeni Jayawickramarajah
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摘要

在这个时代,有很多科技公司专门从事大数据分析,让每一条数据都对最终用户有用。在BP,我们一直在努力构建和利用工具,使我们能够对数据进行有意义的处理——主动消除储层和井堵塞中的缺陷,跟踪油井漏洞,并轻松标记油井性能问题——这样我们就可以将这些数据转化为增值行动。为了实现我们的目标,必须建立一套工具。第一个工具称为生产管理工具(PMT),它利用自动化和机器学习能力实时估计油藏压力和产能指数,使用递减曲线分析概念为每口井生成短期生产曲线,使用井热图标记机会和风险,并使用一个平台优化井的递减。在不使用储层模型的情况下估算致密气田储层压力的方法是前所未有的。它允许实时估计,而不像传统方法那样需要等待时间。同样,对于堵塞后的高气井,递减曲线分析也很困难,而且存在问题,因为没有检测到递减曲线。通过PMT,递减曲线分析概念可以通过模拟井的产能递减而不是其实际速率来实现。这些创新的方法依赖于行业已知的方法,这些方法已经被重新利用和转换,以融入最新的数据科学概念。接下来,构建了一个隐藏缺陷工具,通过量化未采出气率和凝析油延迟,并将这些延迟分配到缺陷类别,主动推动整个油藏和井的缺陷消除。该工具还通过在交互式可视化仪表板中描述生产风险来支持用户启动补救计划。最后,构建了一个漏洞矩阵,将隐藏缺陷的结论与井的性能指标联系起来,通过基于井组或整体风险值的可视化风险严重程度来了解最大的生产威胁。所有这些工具都强调了保持最新技术的重要性,因为它可以为提高技术能力和最大化业务价值提供巨大的潜力。
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Agility in BP Oman – Doing More with Less
In this age, there are many technology companies that specialize in big data analytics, making every piece of data useful to the end user. At BP, we have strived to build and utilize tools that enable us to work meaningfully with our data – to proactively eliminate defects across reservoir and well chokes, to track our well vulnerabilities, and flag well performance issues easily – so we can transform this data into value-adding actions. A suite of tools had to be built to meet our objectives. The first tool, called the Production Management Tool (PMT), utilizes automation and machine learning capability to estimate live reservoir pressure and productivity index in real-time, generate short-term production profiles for every well using a decline curve analysis concept, flag opportunity and risk using a wells heat map, and optimize well decline using one platform. The approach to estimate live reservoir pressure in a tight gas field, without utilizing a reservoir model, is unprecedented. It allows for a live estimation, without any waiting time which is the case with traditional methods. Similarly, decline curve analysis on a high gas capacity wells which are choked back is difficult and questionable, as there is no decline detected. Through PMT, a decline curve analysis concept is possible by modelling declines on a well's capacity rather than its actual rate. These innovative approaches rely on industry known methods that have been repurposed and transformed to incorporate the latest data science concepts. Next, a Hidden Defects Tool was constructed, which proactively drives defect elimination across the reservoir and well chokes by quantifying unproduced gas rate and condensate deferrals and assigning those deferrals to defect categories. This tool also supports the user in initiating remediation plans by describing the production risk in an interactive visual dashboard. Finally, a vulnerability matrix was constructed to link our hidden defects conclusion and well performance metrics to understand the biggest production threats by visualizing the risk severity based on well groupings, or overall risk value. All these tools highlight the importance of remaining current with the latest technology, as it can provide huge potential to advance technical capabilities and maximize business value.
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