Generative AI will have a huge impact across industries.
Amazon Web Services (AWS) thinks so, according to Hussain Shell, the company’s energy enterprise technologist. He said Amazon has invested heavily in the development and deployment of artificial intelligence and machine learning for more than two decades in both customer-facing and artificial intelligence services and internal operations, he said.
“We are about to witness the next wave of widespread adoption of machine learning, and every customer experience and application, including the energy industry, will have the opportunity to be reinvented with generative AI,” Schell told Rigzone. .
“AWS is committed to this 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 purpose-built AI. It helps drive the next wave: service,” he added.
After seeing some of the uses and benefits of generative AI in the energy industry, Shel said AWS believes this technology can help improve operational efficiency, reduce health and safety exposure, improve customer experience, and reduce emissions associated with energy production. He outlined that he believes that it plays a pivotal role in minimizing and accelerate the energy transition.
“Generative AI, for example, could play a pivotal role in addressing operational safety,” Schell said.
“Energy businesses often operate in remote locations, sometimes in hazardous and hazardous environments. It is directly related to reducing exposure to human health and safety,” he added.
“Generative AI will help the industry make significant strides toward this goal. Images from cameras installed on site can scan for potential safety risks, such as defective valves that cause gas leaks. We can send it to the application,” he continued.
Schell said the application can generate recommendations for personal protective equipment and tools and equipment for remediation work, which eliminates the need for an initial on-site trip to identify the problem and He stressed that operational downtime would be minimized and that health and safety exposure would be reduced.
“Another example is reservoir modeling,” Schell 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. GAN generator networks are trained to generate synthetic reservoir models that resemble real-world reservoirs, while discriminating The network is trained to distinguish between real and synthetic reservoir models,” he continued.
Once a generative model is trained, it can be used to generate a number of synthetic reservoir models that can be used for reservoir simulation and optimization, reducing uncertainty and improving hydrocarbon production predictions, Shell says. said Mr.
“These reservoir models can also be used for other energy applications where understanding the subsurface is important, such as geothermal and carbon capture and storage,” Schell said.
Shell highlighted a third example, pointing to generative AI-based digital assistants.
“Data access is an ongoing challenge that the energy industry is trying to overcome, especially considering that much of the data is decades old and stored in a variety of systems and formats,” he said. I was.
“For example, oil and gas companies, through their underground workflows, have had documents for decades in many different formats, including PDFs, presentations, reports, notes, well records, and Word documents. , it takes a lot of time to find useful information,” he added.
“Engineers spend 60% of their time searching for information, according to one of our top five operators. Ingesting all these documents into a generative AI-based solution powered by indexes has significantly increased data access. , so we can make better decisions faster,” continued Schell.
Asked if he expected all oil and gas companies to use some form of generative AI in the future, Schell said yes, but did not define the potential impact of generative AI on the world. He added that it was important to stress that it was still in the early stages of development. energy industry.
“AWS’ goal is to democratize the use of generative AI,” Shell told Rigzone.
“To achieve this, we are giving our customers and partners the flexibility to choose how they want to build with generative AI, including building their own underlying models using dedicated machine learning infrastructure. Leverage pre-trained underlying models as base models to build applications, or use services with built-in generative AI, without requiring specialized knowledge of the underlying models You can,” he added.
“We also provide cost-effective infrastructure and appropriate security controls to simplify deployment,” he continued.
AI, applied through machine learning, has become one of the most transformative technologies of our generation, AWS officials say, “to tackle some of humanity’s most difficult problems, improve human performance, maximize productivity.”
Therefore, using these technologies responsibly is key to fostering continuous innovation, Schell outlined.
AWS attended the Society of Petroleum Engineers (SPE) International Gulf Coast Chapter’s recent Data Science Convention event in Houston, Texas, with the president of Rigzone in attendance. The annual flagship event of the SPE-GCS Data Analytics Research Group was attended by representatives from the energy and technology sectors.
In a statement sent to Rigzone last month, GlobalData pointed out that machine learning has the potential to transform the oil and gas industry..
“Machine learning is a rapidly growing area in the oil and gas industry,” GlobalData said in a 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 its May report on machine learning in oil and gas, GlobalData highlighted several “major players” including BP, ExxonMobil, Gazprom, Petronas, Rosneft, Saudi Aramco, Shell and Total Energy.
As I told Rigzone earlier this month,Andy Wang, 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, and pointed out that it will be an important part of the future of the field. When asked if he thinks more and more oil companies will adopt data science and machine learning, Wang said yes on both counts.
Back in November 2022, OpenAI, a self-described AI research and implementation company with a mission 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 that ChatGPT is a sibling model of InstructGPT trained to follow prompts and provide detailed responses.
In April of this year, Rigzone investigated how ChatGPT will impact oil and gas jobs.Click to view that article here.
To contact the author, please send an email andreas.exarheas@rigzone.com
