If the potential of artificial intelligence (AI) seemed immense a year ago, generative AI radically increases the possibilities.
“Generative AI, and AI in general, has the transformational capability to change the world as we know it,” said Jay Shah, principal of energy marketing and innovation programs at Amazon Web Services (AWS).
Companies are implementing and scaling AI and machine learning (ML) solutions across their enterprises, Shah said.
“We see tremendous adoption, tremendous understanding where use cases can be applied,” he said, such as Leucipa, which resulted from a Baker Hughes-AWS collaboration. Leucipa is an automated field production software solution that uses digital technologies to break down silos.
Song Hou, who heads up CGG’s AI lab, said companies are actively relying on AI to enhance their daily operations.
The view on AI has evolved from “novel to necessity,” he said. At first, companies thought of it as exciting technology. Now, it’s a necessary tool just to remain competitive.
One of the draws of AI is its ability to boost productivity, said Nikunjkumar Patel, vice president of engineering and technology at Oceaneering.
“AI technology is not just for engineers,” he said. “There are a lot of areas where AI will bring productivity.”
For example, Oceaneering is using AI to reduce the time spent in building inspection reports for customers. Previously, it had taken weeks or months to provide reports after an inspection, whereas now it takes hours or days, he said.
SparkCognition CTO Sridhar Sudarsan said AI and cloud technologies are enabling companies to collaborate on industry data through consortia like Open Subsurface Data Universe (OSDU).
According to SparkCognition, collecting more data isn’t synonymous with gaining more insight. AI technology allows organizations to speed up analysis and surface key insights from their data.
“That will give rise to the ability to apply more AI technology capabilities to be able to drive more results, which will then drive more value from a business focus, which will drive more investments, which will drive more scale, more adoption and so on,” he said.
Mehdi Miremadi, a senior partner at McKinsey & Co., said the industry should look at where AI can bring efficiencies and reliability and improve performance and safety.
“AI and generative AI are value creators,” he said.
Goldman Sachs forecast in August that global investment in AI and generative AI would approach $200 billion by 2025, but cautioned that the investments will happen before adoption and efficiency gains drive major gains in productivity.
Of course, generative AI is very much in its early days.
Shah said AWS is developing and previewing generative AI-based prototypes that can help solve business problems. AWS is working on hundreds of use cases and helping customers find the right place to start their generative AI journey, he said.
And AWS announced in June it was investing $100 million in the AWS Generative AI Innovation Center, which is intended to connect AWS AI and machine learning (ML) experts with customers around the globe to help launch new generative AI products, services and processes.
“Generative AI has the potential to impact and change pretty much every function in every business, in every industry. Our customers understand that very well,” Vasi Philomin, VP and General Manager for Generative AI at AWS, said.
In fact, he said, generative AI has become so mainstream that “there’s not a single conversation I have with any customer where it doesn’t take more than a couple of minutes for generative AI to come up.”
The thing about generative AI, he said, is that it democratizes access to computing technology.
“AI is now within the reach of everybody,” Philomin said.
But risks accompany any technology, he noted. AWS is focused on responsible AI, Philomin said, and is a member of the Responsible AI Institute (RAI Institute).
Manoj Saxena served as the first general manager for IBM’s Watson question-answering computer system, introduced in 2010, before founding RAI Institute to temper the public view of perceived threat from AI technology.
Since founding the institute, his own views on AI have “evolved more dynamically and more demonically than I had ever imagined.”
Saxena’s misgivings are tied to the technology advancing much more rapidly than he anticipated.
“The speed at which the technology has advanced to the point that it can create massively powerful businesses and massively powerful harm is really unsettling,” he said.
At the same time, he believes it will remove a lot of boring aspects of jobs, unleashing productivity and creativity. He compared the advances possible due to generative AI to the massive explosion of jazz music creation following the invention of the electronic synthesizer.
“If you were a jazz musician before, you had to have six or seven people you had to hire to create a band and create the music. Now with the synthesizer, you could create it a lot more cheaply because you had this co-pilot guiding you through it,” Saxena said. “I look at generative AI as the synthesizer for human creativity. It is going to do the same thing for business work and office work and custom interactions that tools like the synthesizer did for jazz.”
While there is a lot of disruptive possibility and hype around the potential, McKinsey’s Miremadi said it largely depends on the time frame.
“If you are thinking, ‘What can be done in the next year or two,’ probably some of the hopes that people have on generative AI, particularly as it gets applied across industries, is aggressive and too optimistic,” he said.
But stretch that out to a decade, he said, and many applications are likely to materialize.
Experimentation with generative AI tools has become routine, McKinsey says in its “The state of AI in 2023: Generative AI’s breakout year” report, released in August. The report states that 79% of respondents reported exposure to generative AI for work or outside of work, and 22% reported regularly using it for work, with respondents in the technology sector and in North America reporting the highest use.
Sriram Srinivasan, senior vice president for Halliburton Global Technology, said certain tasks are more suitable for generative AI technologies, and people will be happy to pass off mundane activities to generative AI.
With GitHub Copilot, a generative AI solution that writes code, “our coders are the ones saying, ‘I want it,’” rather than rejecting the technology, he said.
He said expected first uses of generative AI technologies at Halliburton will largely be to summarize data.
James Brady, chief digital officer for Baker Hughes oilfield services and equipment, sees generative AI as potentially useful in terms of digital assistance for engineering, such as generating well plans or reservoir models. While he considers those possibilities “fairly futuristic,” he also can envision a future in which digital assistance can handle a request such as “build me an FPSO for this discovery.”
The need for a digital assist has never been higher.
“There’s not large numbers of people coming into the industry as in the past. At the same time, the challenges of extracting hydrocarbons is getting harder and harder. And when you see things like that converging, less people and harder tasks, it means that yes, these people need digital assistance,” Brady said. “Technology is going to have to enable a better workforce.”
Some jobs will go away, he said, while other jobs will change or be created.
Bill Braun, CIO at Chevron, said it’s hard to predict what changes generative AI will bring, but he does expect things to change. And change can be a concern, particularly for new people considering entering the industry.
He said he recently met with a group of Chevron’s summer interns, many of whom are pursuing ML or software engineering, and they expressed concern about how AI will affect their intended profession and whether they should change careers.
“They asked me pretty candid questions,” he said.
Their situation parallels the industry’s own transition, he said.
“Some might take that as a concern or angst in terms of traditional business, and what does that mean in terms of a lower carbon future as we go through energy transition,” he said. “But we think it’s a heck of an opportunity because the world needs the energy of today, and we know we’re necessary to be a player in delivering energy of the future. I think in the same way that’s what’s going to happen to software engineers.”
While what they do and how they do it will evolve, he said, they’re taking no risks by going into the profession as long as they’re open to adapting alongside technology.
“I think the combination of the changes in energy and the changes that are happening in technology are both moving at a very rapid rate. But if you’re up for the challenge and want to be part of the future, then it’s a great place to be,” Braun said.
Editor’s note: This is the third part of a multi-part series examining the use of artificial intelligence in the oil patch. Read parts one and two here.
