壳牌将人工智能视为其可持续发展目标的燃料

　　这家能源巨头的双云转型包括一个数据湖架构，人工智能首席执行官Dan Jeavons表示，该架构正在促进业务效率，并将证明随着时间的推移减少碳排放的关键。
　　能源巨头面临着政府和消费者减少碳排放的巨大压力。对于跨国石油天然气公司壳牌来说，人工智能可能是实现这一长期目标的关键催化剂。
　　这家总部位于伦敦的能源公司正在进行的数字转型，由一个混合云平台和Databricks 数据湖之家推动，包括一系列人工智能技术，旨在优化业务效率和利润，并随着时间的推移减少碳足迹。
　　壳牌首席人工智能专家Dan Jeavons表示：＂人工智能已经成为我们整个数字转型过程中非常核心的一部分。＂他指出，壳牌与几家人工智能公司合作，包括微软和C3.ai，但自2015年以来一直与Databricks保持着密切的合作关系，大约有20名Databricks员工被分配到壳牌账户。
　　Jeavons在壳牌担任计算科学和数字创新副总裁仅六个月，曾任壳牌数据科学总经理，自2015年以来一直从事数据科学研究。
　　在向壳牌集团首席信息官Jay Crotts汇报工作的新岗位上，Jeavons的任务是利用人工智能以及区块链、物联网和边缘计算等新兴技术，全面审视壳牌未来的技术战略，并帮助引导其致力于在2050年前将碳足迹减少为净零排放能源业务。
　　Gartner人工智能分析师Anthony Mullen表示，壳牌的人工智能实现超出了大多数其他公司的水平。Mullen说，＂就整个组织的初步实验而言，壳牌已经渡过了难关。＂
　　Jeavons的团队拥有数百名数据科学家，他们使用人工智能（主要是在Databricks基于Spark的平台上）编写算法来执行任务，例如改进地下处理的周期时间、优化资产性能、预测各种设备何时和是否会出故障，以及改进为客户提供的服务。
　　Jeavons说：＂鉴于气候变化的威胁，我们需要转向低碳能源系统，数字技术在其中发挥着关键作用。＂他指出，许多二氧化碳监测数据流将通过Databricks人工智能平台。＂为了显著减少能源系统的二氧化碳排放，数字技术是我们可以利用的核心杠杆之一。＂
　　据Jeavons称，壳牌利用数字技术每年将一个液化天然气（LNG）设施的二氧化碳排放量减少多达13万吨，相当于从美国道路上一年减少2.8万辆汽车。
　　他说：＂许多为我们工作的人都有一种强烈的目的感，即真正应用人工智能来加速能源转型，但我不会假装这很容易。＂
　　数据是基础
　　作为数字化转型的一部分，壳牌依靠Microsoft Azure和AWS这两个公共云，以及Docker和Kubernetes集成化技术，为其2100亿美元的石油和天然气业务的各个方面运行越来越先进的工作负载。
　　壳牌公司计算科学和数字创新副总裁Dan Jeavons
　　Jeavons说，这一战略的一个关键方面是公司的基础数据层，可以从多个工具和技术中系统地访问数据。
　　无论他们是在培养技能还是在积累经验，都要从我的业务中获得最重要的技能，为你的技术团队提供知识。
　　Jeavons说：＂拥有双云战略意味着，在如何管理和集成数据方面，你需要一些一致性。当然，现在并不是所有数据都在一个地方，你有各种各样的数据库。但从分析的角度来看，我们正越来越多地将某些类型的数据整合到基于DataRicks的集成湖体系结构中。＂
　　在分析方面，将数据集成到Databricks的Delta Lake的公共层中，并在公共平台中使用Python，可以实现简单查询和经典报告查询与Power BI等可视化工具的集成。华东CIO大会、华东CIO联盟、CDLC中国数字化灯塔大会、CXO数字化研学之旅、数字化江湖-讲武堂，数字化江湖-大侠传、数字化江湖-论剑、CXO系列管理论坛（陆家嘴CXO管理论坛、宁波东钱湖CXO管理论坛等）、数字化转型网，走进灯塔工厂系列、ECIO大会等
　　但在人工智能方面，Jeavons说，＂它还允许你在同一个平台上运行机器学习工作负载，对我来说，这是一个大转变。＂
　　例如，壳牌将其所有全球时间序列数据，如温度、压力、特定设备等信息整合到一个基于Delta Lake的公共云中，使这家能源巨头能够掌握大多数全球资产的脉搏，包括来自炼油厂、上游设施、风电场和太阳能电池板的数据。Jeavons说：＂如今，总计有1.9万亿行数据，这在全球范围内是一个巨大的数字。＂
　　壳牌的人工智能工作还包括进行故障预测，并通过使用机器视觉识别腐蚀来评估其能源资产的完整性。Jeavons说，＂我们也在使用人工智能开发技术来优化资产，使其更高效地大规模运行，并根据历史性能进行优化。＂他指出，虽然壳牌的人工智能魔力很大程度上归功于其数据湖的实施，但如果没有云技术的进步，这一切都无法实现。他说，＂真的，关键是云的成熟，以及我们能够移除一些额外的层，以便直接从工厂获取数据并将其流式传输到云中。这有助于推动数据分析和人工智能战略。＂
　　前面的路
　　壳牌公司总共有大约350名专业数据科学家和大约4000名专业软件工程师，他们在位于印度班加罗尔的一个壳牌中心或在英国、荷兰和德克萨斯州休斯顿远程工作。
　　除了云和数据湖之家，壳牌还转向了Microsoft Azure DevOps等高级开发工具，并将GitHub整合到其开发者的工作方式中。Jeavons说，它还在为云部署更成熟的代码筛选工具，运行适当的CI/CD工作流，并将人工智能作为其远程监控中心的一部分，在全球范围内监控10000台设备的运行。
　　但Jeavons说，正是一种通用的数据湖之家体系结构的开发带来了最大的不同，给了壳牌＂一个集成的数据层，以一致的方式提供了整个业务中所有数据的可见性＂。
　　Jeavons说，＂我们很早就采用了Delta，有一段时间，它更多地是在概念验证模式下，而不是在大规模负载下部署。在过去18个月里，我们确实看到了一个阶段性的变化，我们一直在非常努力地运行。＂
　　然而，变革管理仍然是该公司面临的最大挑战之一。
　　Jeavons说：＂你如何将技术嵌入到业务流程中，使其可用，成为每天发生的事情的一部分，并开发有效的算法？我不会低估它的难度，这不是小事。大规模推广人工智能更难，这仍然是一段非常漫长的旅程，我们已经取得了一些进展，但还有很多事情要做。＂
　　原文：
　　Energy giants are under significant pressure by governments and consumers to reduce carbon emissions. For multinational oil and gas company Shell, artificial intelligence may be a key catalyst for fulfilling that long-term goal.
　　The London-headquartered energy company’s ongoing digital transformation, fueled by a hybrid cloud platform and Databricks data lake house, includes a mix of AI technologies aimed at optimizing business efficiencies and profits and, over time, reducing its carbon footprint.
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　　＂AI has become a very core part of our overall digital transformation journey,＂ says Shell’s chief AI guru Dan Jeavons, noting that Shell works with several AI companies, including Microsoft and C3.ai, but has been in a close partnership with Databricks since 2015. Roughly 20 Databricks employees are assigned to the Shell account.
　　Jeavons, who has served as vice president of computational science and digital innovation at Shell for just six months, is the former general manager of data science at Shell and has been knee deep in data science since 2015.
　　In his new role, reporting to Shell Group CIO Jay Crotts, Jeavons is tasked with employing AI as well as emerging technologies such as blockchain, IoT, and edge computing to overhaul Shell’s future technology strategy and help steer its commitment to reduce its carbon footprint to become a net-zero emissions energy business by 2050.
　　Gartner AI analyst Anthony Mullen says Shell’s AI implementations are beyond what most other companies are doing. ＂Shell is over the hump in terms of initial experimentation right across the organization,＂ says Mullen, pointing to Shell’s Center for Excellence and participation in OpenAI.
　　Jeavons’ group has several hundred data scientists using AI — mostly on Databricks’ Spark-based platform — writing algorithms to execute tasks such as improving the cycle times of subsurface processing, optimizing the performance of assets, predicting when and if various pieces of equipment might fail, as well as improving offerings to customers.
　　＂Given the threat of climate change, we need to move to a lower carbon energy system and digital plays a key role in that,＂ Jeavons says, noting many of the CO2 monitoring data streams will flow through Databricks AI platform. ＂Digital technology is one of the core levers that we can pull in order to significantly reduce the CO2 footprint of the energy system.＂
　　According to Jeavons, Shell’s use of digital technology reduced the CO2 emissions of one liquefied natural gas (LNG) facility by as much as 130 kilotons per year — equivalent to removing 28,000 US vehicles off the road for a year.
　　＂Many of the people that work for us have a sense of compelling purpose actually applying AI to try to accelerate energy transition,＂ he says. ＂But I’m not going to pretend it’s easy.＂ Data is the foundation
　　As part of its digital transformation, Shell relies on two public clouds, Microsoft Azure and AWS, as well as Docker and Kubernetes containerization technologies, to run increasingly advanced workloads for various aspects of its $210 billion oil and gas business.
　　Dan Jeavons, VP of computational science and digital innovation, Shell
　　A key facet of that strategy, Jeavons says, is the company’s foundational data layer — a pool from which multiple tools and technologies can access data systematically.
　　＂Having a dual-cloud strategy means you need some consistency as to how you want to manage and integrate your data. Now of course, not all data is going to be in one place. You have a variety of databases; everybody does,＂ Jeavons says. ＂But from an analytics perspective, more and more, we’re consolidating certain types of data into an integrated lake house architecture based on Databricks.＂
　　On the analytics side, integrating data into a common layer in Databricks’ Delta Lake and using Python in a common platform allows simple queries and classical reporting query integration with visualization tools such as Power BI.
　　But on the AI front, it ＂also allows you to run the machine learning workloads all on the same platform,＂ Jeavons says. ＂For me, that’s been a step change.＂
　　For example, Shell has integrated all its global time-series data — information such as temperature, pressure, a particular piece of equipment — into a common cloud based on Delta Lake, enabling the energy giant to keep its finger on the pulse of most global assets, including data from refineries, plants, upstream facilities, winds farms, and solar panels. ＂It’s 1.9 trillion rows of data aggregated today, which is a huge amount globally,＂ Jeavons says. ＂We measure everywhere.＂
　　Shell’s AI efforts also include performing failure predictions and assessing the integrity of its energy assets by using machine vision to identify corrosion. ＂We’re also using AI to develop technology which can optimize the assets and make them run more efficiently at scale and optimize based on historical performance,＂ Jeavons says, noting that, while much of Shell’s AI magic is due the implementation of its data lake, none of it could be achieved without cloud advancements.
　　＂Really, the key thing has been the maturing of the clouds and the ability to remove some additional layers that we had [in order] to take data directly from the plants and stream it into the cloud. That’s been helpful in driving both data analytics but also the AI strategy,＂ he says. The road ahead
　　In total, Shell has about 350 professional data scientists and roughly 4,000 professional software engineers working remotely and/or in one of Shell’s hubs in Bangalore, India; the UK; the Netherlands, and Houston, Texas.
　　Aside from the cloud and data lake house, Shell has also moved to advanced development tools such as Microsoft Azure DevOps and is integrating GitHub into its developers’ ways of working. It is also deploying more mature code screening tools for the cloud, running ＂proper＂ CI/CD workflows and monitoring ＂north＂ of 10,000 pieces of equipment globally using AI as part of its remote surveillance centers, Jeavons says.
　　But it is the development of a common lake house architecture that has made the most difference, giving Shell ＂an integrated data layer that provides visibility of all the data across our business＂ in a consistent way, Jeavons say.
　　＂We were a very early adopter of Delta,＂ he says. ＂For a while, it was more in proof-of-concept mode than in deployed at scale load. It’s really been in the past 18 months where we’ve seen a step change and we’ve been running quite hard.＂
　　Change management, however, remains one of the company’s biggest challenges.
　　＂How do you embed the technology into the business process and make it usable and a part of what happens every day and developing algorithms that work? I’m not going to underplay how difficult it is. It’s non-trivial,＂ Jeavons says. ＂It’s tougher to develop the adoption [of AI] at scale. It’s still very much a journey and we’ve made some strides but there’s a lot more to do.＂
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