人工智能和理解一切的道路

一种新的自动化现场生产解决方案使用数字技术来打破孤岛。

Baker Hughes 和 AWS 计划在今年晚些时候发布一款新的软件解决方案,用于自动化现场生产优化。(来源:贝克休斯)

随着该行业努力解决可靠性、可负担性和可持续性的能源三难问题,管理油气田的生产变得前所未有的重要。

但由于数据孤立,优化生产管理并不容易。每个水库都是不同的,这增加了复杂性。每一个发展都是独特的。而且技术选择差异很大。

人工智能 (AI)、机器学习 (ML) 和云计算等数字技术的最新进展为创建可扩展、自动化和数据驱动的石油和天然气生产开辟了新途径。 

Leucipa 由 Baker Hughes 和 Amazon Web Services (AWS) 创建,以希腊哲学家 Leucippus 的名字命名,他观察到:“任何事情的发生都是随机的,但一切都是有原因和必然的。”

Howard Gefen,AWS 能源与公用事业总经理
“借助数字解决方案,借助数据、人工智能和机器学习工具,我们现在可以理解一切,并提供数据驱动的见解和建议。”AWS 能源与公用事业总经理奥瓦德·格芬 (Oward Gefen)。(来源:AWS)

但了解油田等所有事物如何组合在一起并不是一件容易的事。

Leucipa 将 AWS 云功能与 Baker Hughes 的石油和天然气专业知识结合到了一款产品中,两家公司表示,该产品将帮助运营商优化油气田的生产。

“生产是一个复杂的领域,”AWS 能源和公用事业总经理霍华德·格芬 (Howard Gefen) 表示。

该行业在优化生产的各个部分方面做得很好,但由于孤立的数据和解决方案,无法“理解用于优化油田生产的所有部分的相互依赖性” , 他说。

“借助数字解决方案,借助数据、人工智能和机器学习工具,我们现在可以理解一切并提供数据驱动的见解和建议,”格芬说。

“分散的方法”

Baker Hughes 首席数字官 James Brady 表示,运营商正在寻找利用技术降低起重和运营成本的方法,通常是通过运营支出优化或扩展。

“它需要是能够产生影响并收回成本的技术,”他说。“你需要研究技术和工具,使能源公司能够生产和提取并做出生产承诺,但需要更少的人员。”

他表示,过去生产软件“非常分散”,没有一家公司能够覆盖所有基地。他说,有些任务可以胜任:油井或油藏预测、历史匹配或定制现场解决方案。

“你只会看到一种真正分散的方法,而且它往往沿着技术轴。因此,公司将问题分为不同的部分。没有人攻击整个事情,”他说。“贝克休斯有一些作品,但我们也没有全部。”

在行业中,变化是规则:尤其是在不同的现场以及使用许多不同的设备的情况下。

James Brady,贝克休斯数字化高级副总裁
“这就是生产软件变得困难的部分原因,它不是使用标准模板的产品游戏。”贝克休斯数字高级副总裁布雷迪说道。(来源:贝克休斯)

“这就是生产软件变得困难的部分原因,”布雷迪说。“这”不是一个使用标准模板的产品游戏。使生产变得困难的另一件事是每个领域的基础设施都不同。你必须轻轻一触就进入那里,能够连接和自动化,然后能够快速进入那里并快速处理与该领域相关的数据。”

贝克休斯希望倾听市场的声音并整合技术来解决未满足的生产软件需求,因此该公司委托麦肯锡公司进行市场研究,了解公司对生产软件的需求和需求以及哪些需求未得到满足。

“排名第一的生产软件工具是什么?我想我们大多数人都知道,占主导地位的市场参与者是 [Microsoft] Excel 电子表格,”布雷迪说。

他说,快速进入市场意味着合作。

Brady 表示,“我们不是一家云公司,而是一家拥有强大技术愿景的云公司”,它了解能源解决方案,并且有能力帮助应用他们在其他地方看到的模式。他补充说,AWS 知道如何构建可扩展的解决方案,并且拥有在不同领域构建人工智能和机器学习框架的经验。

“从某种意义上说,他们是推动者,但他们也是技术合作伙伴,帮助指导我们迈向这一旅程的下一步,”他说。

Gefen 表示 Leucipa 利用先进的分析以及人工智能和机器学习工具。

Leucipa 是“我们有机会利用现有的云架构和开放架构来创建一个平台,将所有这些孤岛和数据位以及这些相互依赖性结合在一起,”他说。

贝克综合领域
Baker Hughes 和 AWS 表示,Leucipa 使用数字技术打破阻碍现场生产优化自动化的孤岛。(来源:贝克休斯)

他说,其结果是一个可以扩展和自动化生产操作的解决方案。

在早期采用者将数据引入 Leucipa 后,“他们将能够了解数据告诉他们应该做什么。” 最初的重点是他们的井,如何在单井的基础上优化它们,并最终优化一组井,”布雷迪说。“云真正使我们能够做的事情之一就是能够查看很多场景。”

布雷迪说,复杂的建模是通过自动化实现的更有趣的场景之一,无论是增加产量还是在减少碳排放的同时保持产量稳定。

“这是一种与我们以前不同类型的基于约束的建模,”他说。“我认为这需要云真正探索解决方案空间,帮助我们解决三难困境的这两部分。”

Baker Hughes 和 AWS 在 2 月份宣布了他们的 Leucipa 解决方案,Pan American 成为第一家公开承诺与 Leucipa 合作的公司。此后,已有近十几家公司签约。

Brady 表示,最初的版本仍在开发中,但预计早期采用者将在年中左右首次体验 Leucipa。

原文链接/hartenergy

AI and the Path Toward Making Sense of Everything

A new automated field production solution uses digital technologies to break down silos.

Baker Hughes and AWS are slated to release a new software solution for automating field production optimization later this year. (Source: Baker Hughes)

As the industry grapples with the energy trilemma of reliability, affordability and sustainability, managing production at oil and gas fields has never been more important.

But optimally managing that production has not been easy because of siloed data. Adding to the complexity, each reservoir is different. Each development is distinctive. And technology selection varies widely.

Recent advancements in digital technology, such as artificial intelligence (AI), machine learning (ML) and cloud computing have opened up new avenues to create scalable, automated and data-driven oil and gas production. 

Leucipa, created by Baker Hughes and Amazon Web Services (AWS), is named after the Greek philosopher Leucippus, who observed: “Nothing occurs at random, but everything for a reason and by necessity.”

Howard Gefen, General Manager – Energy & Utilities, AWS
“With digital solutions, with data and AI and ML tools, we can now sort of make sense of everything and provide data-driven insights and recommendations.” —Howard Gefen, general manager of energy & utilities, AWS. (Source: AWS)

But understanding how everything in, for example, an oil field, fits together is no small task.

Leucipa combines AWS cloud capabilities with Baker Hughes’ oil and gas expertise in a product that the companies say will help operators optimize production of oil and gas fields.

“Production is a complex domain,” Howard Gefen, general manager for energy and utilities at AWS, said.

The industry has done a good job of optimizing the individual parts of production, but due to siloed data and solutions, has not been able to “make sense of the interdependencies” of all the pieces that feed into optimizing a field’s production, he said.

“With digital solutions, with data and AI and ML tools, we can now sort of make sense of everything and provide data-driven insights and recommendations,” Gefen said.

‘Scattered approach’

James Brady, chief digital officer at Baker Hughes, said operators are looking for ways to use technology to reduce lifting and operating costs, often through opex optimization or extension.

“It needs to be technology that makes an impact and pays for itself,” he said. “You need to look at the technology and tools that allow energy companies to be able to produce and extract and make their production commitments, but with less people.”

He said production software has been “very scattered” in the past and there’s not been a single company that covers all the bases. Some perform tasks competently: well or reservoir forecasting, history matching or bespoke field solutions, he said.

“You just see a real scattered approach, and it tends to be along the technology axis. So companies divide different parts of the problems. Nobody attacks the whole thing,” he said. “Baker Hughes has some pieces, but we don't have it all, either.”

In the industry, variation is the rule: particularly field to field and with many different pieces of equipment at work.

James Brady, Senior VP – Digital, Baker Hughes
“That's part of what makes production software hard, it’s not a product play with a standard template.” —James Brady, senior vice president of digital, Baker Hughes. (Source: Baker Hughes)

“That's part of what makes production software hard,” Brady said. “It’s not a product play with a standard template. The other thing that makes production hard is every field is different with respect to its infrastructure. You kind of have to go in there with a light touch, be able to connect and automate and then quickly be able to get in there and do something with data quickly that's relevant to that field.”

Baker Hughes wanted to listen to the market and put together technology that addressed the unmet production software needs, so the company commissioned McKinsey & Co. to carry out a market study on what companies need and want from production software — and which needs were unmet.

“What's the number one production software tool? I think probably most of us that have been around know that the dominant market player is [Microsoft] Excel spreadsheets,” Brady said.

Getting to market quickly meant collaborating, he said.

“We're not a cloud company —AWS is, and one with a strong technology vision,” an understanding of solutions for energy and the ability to help apply patterns they have seen elsewhere, Brady said. AWS knows how to build scalable solutions and has experience building AI and ML frameworks in different domains, he added.

“In some sense, they're an enabler, but they're also a technology partner to help guide us to the next step of this journey,” he said.

Gefen said Leucipa leverages advanced analytics and AI and ML tools.

Leucipa is “an opportunity for us to leverage what's available now in terms of cloud architecture and open architecture to create a platform that brings all these silos and bits of data and these interdependencies together,” he said.

Baker Integrated field
Leucipa uses digital technologies to break down siloes that have impeded the automation of field production optimization, according to Baker Hughes and AWS. (Source: Baker Hughes)

The result is a solution that can scale and automate production operations, he said.

After the early adopters bring their data into Leucipa, “they'll be able to learn something about what that data is telling them that they should do. The initial focus is their wells, how they can be optimized on a single-well basis and eventually a set of wells together,” Brady said. “One of the things that cloud really enables us to do is this ability to look at a lot of scenarios.”

Sophisticated modeling is one of the more interesting scenarios made possible through automation—whether on increasing production or holding it steady while decreasing carbon emissions, Brady said.

“It's a different type of constraint-based modeling than we did before,” he said. “I think that will require the cloud to really explore the solution space that helps us honor those two parts of the trilemma.”

Baker Hughes and AWS announced in February their Leucipa solution and Pan American as the first company to publicly commit to working with Leucipa. Since then, nearly a dozen companies have signed on.

The initial release is still under development, but the expectation is that the early adopters will get their first taste of Leucipa around mid-year, Brady said.