AWS for Industries

Rethinking Energy with Generative AI

Over the course of the last two decades, digitalization across the energy industry has enabled new levels of operational efficiency and productivity while also helping to reduce safety risk and environmental impact. Cloud computing and the ready availability of scalable compute capacity, combined with the rapid advancement of machine learning (ML) technologies, have enabled the energy industry to leverage the massive amounts of data that it generates to completely transform how it operates.

But that was just the beginning. The next wave of widespread adoption of ML will be driven by generative artificial intelligence (AI), and every customer experience and application across the energy value chain will be reinvented as a result.

Understanding Generative AI

Generative AI is a subset of traditional ML and is powered by ultra-large models such as large language models (LLMs) and multimodal models (such as texts, images, video, and audio). Generative AI enables new capabilities, such as code generation and the creation of images based on natural language prompts, and can transform existing ML-powered capabilities, such as web searches and chatbots. Generative AI is powered by ML models—very large models that are pretrained on vast amounts of data and commonly referred to as foundation models (FMs).

Some of the top use cases for generative AI include content generation, content summarization, conversational AI, question answering, and education. Given these innovative capabilities, industries such as media and entertainment, financial services, healthcare and life sciences, and education are expected to see the most profound impacts early on—but that doesn’t mean that generative AI is not applicable to the energy industry or that its impact will be any less profound.

Generative AI and Energy

The energy industry currently faces a trilemma of generational challenges: ensuring energy security, providing affordable energy, and moving to a cleaner and more sustainable energy future. Digitalization is integral to addressing these generational challenges. In its 2022 Digitalization report, the International Energy Agency (IEA) states that digitalization across the energy sector could help cut costs, improve efficiencies and resilience, and reduce emissions. While it is still too early to fully quantify or define the potential impact that generative AI will have on the energy industry, it’s clear that the new capabilities enabled by this groundbreaking technology will further equip energy organizations with the tool kit they need to overcome these generational challenges.

Generative AI will be one of the most transformational technologies of our generation and will help us tackle some of humanity’s biggest challenges. Regarding the future of the energy industry, we envision generative AI playing a pivotal role in helping to increase operational efficiencies, reduce health and safety exposure, enhance the customer experience, minimize the emissions associated with energy production, and ultimately accelerate the energy transition.

Energy Industry Use Cases

For the energy industry, generative AI-based systems will initially serve as chat or natural-language interfaces that complement existing ML- and AI-based systems. But the potential for generative AI across the energy value chain is enormous. Here are a few of the generative AI use cases that we see on the horizon for the energy industry.

Knowledge management. Data discovery is one of the most pervasive challenges for the energy industry. Data sets are often decades old and reside in various formats and siloed storage systems. According to one of the top five energy operators, engineers spend 60 percent of their time searching for data. If this data was ingested and integrated into a generative AI-based solution augmented by an index, employees could use natural language prompts to more quickly retrieve stored internal knowledge. This would improve data search and discovery and drastically decrease decision-making time while also ensuring knowledge transfer from one generation of energy workers to the next.

Operational efficiencies. Energy operations often occur in remote and sometimes hazardous conditions. The industry has long sought solutions that could reduce trips to the field, which would translate into minimized health and safety exposure for workers, reduced operational downtime and increased efficiencies, and a reduced environmental footprint as a result of eliminating the emissions associated with traveling to and from a field location.

Moreover, generative AI is capable of classifying images and summarizing videos. Images and videos streamed from cameras stationed at field locations could be ingested and analyzed using a generative AI application, which could scan and send alerts about operational anomalies or safety hazards. The application could also provide recommendations for personal protective equipment for workers, in addition to processes and tools for remedial work to address an identified issue. This would help to reduce trips to the field, minimize operational downtime, and reduce health and safety exposure.

Subsurface modeling. General adversarial networks (GANs) are a popular generative AI technique that could be used to generate synthetic subsurface models. The generator network of the GAN could be trained to produce synthetic models that are similar to real-world subsurface reservoirs, while the discriminator network would be trained to distinguish between real and synthetic reservoir models. Once the generative model is trained, it could be used to generate a large number of synthetic reservoir models that could be used for reservoir simulation and optimization, reducing uncertainty and improving hydrocarbon production forecasting. These reservoir models could also be used for other energy applications where subsurface understanding is critical, such as geothermal and carbon capture and storage.

AWS and the Cloud

Over the last 25 years, Amazon has invested heavily in the development and deployment of AI and ML. This includes customer-facing services like the recommendation engines that personalize the shopping experience on and internal operations like the AI-powered robots that optimize order fulfillment in warehouses.

And now Amazon Web Services (AWS) will help drive the adoption of generative AI across the energy industry by making it easy, practical, and cost-effective for organizations to leverage it across their businesses. AWS is focused on democratizing the use of generative AI by providing customers and partners with the flexibility to choose both the way they want to build cost-efficient infrastructure as well as the correct security controls to help simplify deployment.

To help drive this adoption, we’ve launched Amazon Bedrock, a new fully managed service for building and scaling generative AI applications. This service provides API access to pretrained FMs from innovative AI startups, such as AI21 Labs, Anthropic, and Stability AI, and first-party Amazon Titan FMs. Amazon Bedrock provides organizations with an easy way to build and scale generative AI-based applications using FMs. Bedrock (currently available in limited preview) will offer organizations the ability to access a range of powerful FMs for text and images.

Generative AI FMs require performant, cost-effective infrastructure that is purpose-built for ML. AWS customers use Amazon Elastic Compute Cloud (Amazon EC2) Trn1 Instances, powered by AWS Trainium, which help to deliver up to 50 percent savings on training costs over comparable Amazon EC2 instances. AWS customers also have the option of utilizing NVIDIA GPUs to cost-effectively scale infrastructure to train and run FMs containing hundreds of billions of parameters. Once the generative AI models are deployed at scale, most costs will be associated with running the models and doing inference. This is when customers could use Amazon EC2 Inf2 instances powered by AWS Inferentia2, which are optimized specifically for large-scale generative AI applications with models containing hundreds of billions of parameters.

To learn more about how AWS is helping its customers and partners transform, innovate, and accelerate the energy transition, please visit here. And to learn more about how AWS approaches generative AI, please visit here.

Kumar Lakshmipathi

Kumar Lakshmipathi

Kumar is a technology leader, AI/ML strategist, and cloud architect with 25 years of experience delivering innovative enterprise solutions. At AWS, Kumar helps lead Generative AI initiatives for energy customers globally. Prior to AWS, Kumar was a key player in growing two startups from the ground up, serving pivotal roles in strategy, sales, talent acquisition, and product management. Kumar is based out of Houston, is wildly optimistic about the future and an ardent believer in happiness through technology.

Hussein Shel

Hussein Shel

Hussein Shel is Director, Chief Technologist and Head of Upstream for Energy at Amazon Web Services. His role champions the voice of leaders of enterprise customers and prospects, and, conversely, the voice of AWS back to the enterprises on AWS forward thinking and technology innovations in Energy. He joined AWS recently from Microsoft where he was Chief Technology Officer for Energy & Resources and Director of Strategic Customer Engagements partnering with many Energy companies around the world on their Digital Transformation and Energy Transition journey and where he led the delivery of Microsoft Cloud High Performance Computing platform focusing on the Oil & Gas industry. Previously, Hussein spent 10 years with Chevron where he worked in multiple business units in Upstream and Midstream shaping many of Chevron’s key technology strategies around Drilling and Completions, Production and Operations and Reservoir Management across their Energy assets.

Jay Shah

Jay Shah

Jay Shah is an experienced growth strategist with demonstrated experience across digital marketing, pricing, product innovation, and P&L management. Jay is currently Principal, Energy Marketing and Innovation Programs at Amazon Web Services (AWS). Prior this role, he was the Head of Commercial and Marketing at Direct Energy responsible for product, marketing, digital, and pricing for all brands across North America. Jay has over 20 years of leadership and experience across the biotechnology, consumer goods, energy, and technology industries. He has previously held roles in management consulting, corporate strategy, GM, commercial/P&L, and M&A.