Generative AI Will Have Profound Impact Across Sectors

Generative AI will have a profound impact across industries.

That’s what Amazon Web Services (AWS) believes, according to Hussein Shel, an Energy Enterprise Technologist for the company, who said Amazon has invested heavily in the development and deployment of artificial intelligence and machine learning for more than two decades for both customer-facing services and internal operations.

We are now going to see the next wave of widespread adoption of machine learning, with the opportunity for every customer experience and application to be reinvented with generative AI, including the energy industry,” Shel told Rigzone.

“AWS will help drive this next wave by making it easy, practical, and cost-effective for customers to use generative AI in their business across all the three layers of the technology stack, including infrastructure, machine learning tools, and purpose-built AI services,” he added.

Looking at some of the applications and benefits of generative AI in the energy industry, Shel outlined that AWS sees the technology playing a pivotal role in increasing operational efficiencies, reducing health and safety exposure, enhancing customer experience, minimizing the emissions associated with energy production, and accelerating the energy transition.

“For example, generative AI could play a pivotal role in addressing operational site safety,” Shel said.

“Energy operations often occur in remote, and sometimes hazardous and risky environments. The industry has long-sought solutions that help to reduce trips to the field, which directly correlates to reduced worker health and safety exposure,” he added.

“Generative AI can help the industry make significant strides towards this goal. Images from cameras stationed at field locations can be sent to a generative AI application that could scan for potential safety risks, such as faulty valves resulting in gas leaks,” he continued.

Shel said the application could generate recommendations for personal protective equipment and tools and equipment for remedial work, highlighting that this would help to eliminate an initial trip to the field to identify issues, minimize operational downtime, and also reduce health and safety exposure.

“Another example is reservoir modeling,” Shel noted.

“Generative AI models can be used for reservoir modeling by generating synthetic reservoir models that can simulate reservoir behavior,” he added.

“GANs are a popular generative AI technique used to generate synthetic reservoir models. The generator network of the GAN is trained to produce synthetic reservoir models that are similar to real-world reservoirs, while the discriminator network is trained to distinguish between real and synthetic reservoir models,” he went on to state.

Once the generative model is trained, it can be used to generate a large number of synthetic reservoir models that can be used for reservoir simulation and optimization, reducing uncertainty and improving hydrocarbon production forecasting, Shel stated.

“These reservoir models can also be used for other energy applications where subsurface understanding is critical, such as geothermal and carbon capture and storage,” Shel said.

Highlighting a third example, Shel pointed out a generative AI based digital assistant.

“Data access is a continuous challenge the energy industry is looking to overcome, especially considering much of its data is decades old and sits in various systems and formats,” he said.

“Oil and gas companies, for example, have decades of documents created throughout the subsurface workflow in different formats, i.e., PDFs, presentations, reports, memos, well logs, word documents, and finding useful information takes a considerable amount of time,” he added.

“According to one of the top five operators, engineers spend 60 percent of their time searching for information. Ingesting all of those documents on a generative AI based solution augmented by an index can dramatically improve data access, which can lead to making better decisions faster,” Shel continued.

When asked if the thought all oil and gas companies will use generative AI in some way in the future, Shel said he did, but added that it’s important to stress that it’s still early days when it comes to defining the potential impact of generative AI on the energy industry.

“At AWS, our goal is to democratize the use of generative AI,” Shel told Rigzone.

“To do this, we’re providing our customers and partners with the flexibility to choose the way they want to build with generative AI, such as building their own foundation models with purpose-built machine learning infrastructure; leveraging pre-trained foundation models as base models to build their applications; or use services with built-in generative AI without requiring any specific expertise in foundation models,” he added.

“We’re also providing cost-efficient infrastructure and the correct security controls to help simplify deployment,” he continued.

The AWS representative outlined that AI applied through machine learning will be one of the most transformational technologies of our generation, “tackling some of humanity’s most challenging problems, augmenting human performance, and maximizing productivity”.

As such, responsible use of these technologies is key to fostering continued innovation, Shel outlined.

AWS took part in the Society of Petroleum Engineers (SPE) International Gulf Coast Section’s recent Data Science Convention event in Houston, Texas, which was attended by Rigzone’s President. The event, which is described as the annual flagship event of the SPE-GCS Data Analytics Study Group, hosted representatives from the energy and technology sectors.

Last month, in a statement sent to Rigzone, GlobalData noted that machine learning has the potential to transform the oil and gas industry.

“Machine learning is a rapidly growing field in the oil and gas industry,” GlobalData said in the statement.

“Overall, machine learning has the potential to improve efficiency, increase production, and reduce costs in the oil and gas industry,” the company added.

In a report on machine learning in oil and gas published back in May, GlobalData highlighted several “key players”, including BP, ExxonMobil, Gazprom, Petronas, Rosneft, Saudi Aramco, Shell, and TotalEnergies.

Speaking to Rigzone earlier this month, Andy Wang, the Founder and Chief Executive Officer of data solutions company Prescient, said data science is the future of oil and gas.

Wang highlighted that data sciences includes many data tools, including machine learning, which he noted will be an important part of the future of the sector. When asked if he thought more and more oil companies would adopt data science, and machine learning, Wang responded positively on both counts.

Back in November 2022, OpenAI, which describes itself as an AI research and deployment company whose mission is to ensure that artificial general intelligence benefits all of humanity, introduced ChatGPT. In a statement posted on its website on November 30 last year, OpenAI said ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.

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