Generative AI will have a profound impact on all industries.
That’s what Amazon Web Services (AWS) believes, according to Hussein Shel, the company’s energy business technologist, who said Amazon has invested heavily in the development and deployment of artificial intelligence and machine learning during more than two decades for both customers. services and internal operations.
“We will now see the next wave of widespread machine learning adoption, 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 three layers of the technology stack, including infrastructure, machine learning tools and artificial intelligence specifically created services”, he added.
Discussing some of the applications and benefits of generative AI in the energy industry, Shel highlighted that AWS sees the technology as playing a critical role in increasing operational efficiency, reducing exposure to health and safety, improving the customer experience and minimizing emissions associated with energy production. and accelerate the energy transition.
“For example, generative AI could play a critical role in addressing operational site security,” Shel said.
“Energy operations often occur in remote, and sometimes dangerous and risky environments. The industry has long sought solutions that help reduce field travel, which directly correlates to reduced exposure to the health and safety of workers,” he added.
“Generative AI can help the industry make significant progress towards this goal. Images from cameras located at field sites can be fed to a generative AI application that could look for potential safety risks, such as faulty valves that cause gas leaks,” he continued.
Shel said the app could generate recommendations for personal protective equipment and tools and equipment for remedial work, noting that this would help eliminate an initial trip to the field to identify problems, minimize operational downtime and also reduce exposure to health and safety.
“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 GAN’s network of generators is trained to produce synthetic reservoir models that are similar to real-world reservoirs, while the discrimination network is trained to distinguish between real and synthetic deposit models,” he said.
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 said.
“These reservoir models can also be used for other energy applications where understanding the subsurface is critical, such as carbon capture and storage and geothermal,” Shel said.
Highlighting a third example, Shel pointed to a digital assistant based on generative AI.
“Access to data is an ongoing challenge that the energy industry wants to overcome, especially given that much of its data is decades old and in multiple systems and formats,” he said.
“Oil and gas companies, for example, have decades of documents created throughout the underground workflow in various formats, i.e. PDFs, presentations, reports, memos, well logs, Word documents and finding useful information it takes a considerable amount of time.” added.
“According to one of the top five operators, engineers spend 60 percent of their time searching for information. Ingesting all these documents into a generative AI-based solution augmented by an index can dramatically improve data access, which can lead to better decisions faster,” Shel continued.
Asked if he thinks every oil and gas company will use generative AI in some way in the future, Shel said yes, but added that it’s important to stress that it’s still early days. when it comes to defining the potential impact of generative artificial intelligence on the energy industry.
“At AWS, our goal is to democratize the use of generative AI,” Shel told Rigzone.
“To do this, we give our customers and partners the flexibility to choose how they want to build with generative AI, such as building their own base models with a machine learning infrastructure designed for this; leveraging the models of pre-trained foundations as basic models to build their applications; or use services with built-in generative artificial intelligence without requiring any specific expertise in foundation models,” he added.
“We also provide cost-effective infrastructure and the right security controls to help simplify deployment,” he continued.
The AWS representative emphasized that AI applied through machine learning will be one of the most transformative technologies of our generation, “tackling some of humanity’s toughest problems, increasing human performance and maximizing productivity.” .
As such, the responsible use of these technologies is key to fostering continuous innovation, Shel emphasized.
AWS participated in the recent Society of Petroleum Engineers (SPE) Gulf Coast International Section Data Science Convention event in Houston, Texas, which was attended by the president of Rigzone. The event, described as the SPE-GCS Data Analytics Study Group’s annual flagship event, 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 in May, GlobalData highlighted several “key players”, including BP, ExxonMobil, Gazprom, Petronas, Rosneft, Saudi Aramco, Shell and TotalEnergies.
Speaking to Rigzone earlier this monthAndy Wang, the founder and CEO of data solutions company Prescient, said data science is the future of oil and gas.
Wang emphasized that data science includes many data tools, including machine learning, which he noted will be an important part of the industry’s future. When asked if he thought more and more oil companies would adopt data science and machine learning, Wang answered positively on both counts.
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 sister model to InstructGPT, which is trained to follow an instruction in a message and provide a detailed response.
In April this year, Rigzone analyzed how ChatGPT will affect oil and gas jobs. To view this article, click here.
To contact the author, please send an email andreas.exarcheas@rigzone.com
