Deliver a reliable supply of lower cost fuels and power, while optimizing energy efficiency.
To meet global demands, energy companies are turning to a software-defined approach is needed to explore, produce, transport, and deliver lower-cost energy while pursuing net-zero emission goals. They’re leveraging AI and high-performance computing (HPC) to reduce environmental impact from subsurface operations, automate manually intensive surface operations, and bring real-time intelligence to the grid edge.
Learn how Shell used NVIDIA DGX™ systems to determine salt boundaries in reservoir modeling, enable 4K iterative image reconstruction, test new designs for industrial plants, and drive advancements in sustainable new materials.
Siemens Gamesa is optimizing their offshore wind farms for maximum power output at minimal cost using NVIDIA Omniverse™ and NVIDIA Modulus. Find out how neural super-resolution accelerates simulation times from 40 days to 15 minutes.
Accelerate reservoir simulation and seismic processing for fuel production.
Learn how AI is accelerating reservoir simulation and seismic processing, enhancing pipeline monitoring, and protecting worker health and safety, while reducing emissions and environmental impact.
Build industrial and scientific digital twins for sustainability and safety.
Find out how AI is being used to develop physically accurate industrial digital twins, scale renewable energy generation, simulate climate and weather, speed up computational fluid dynamics (CFD) workloads, and optimize industrial site efficiency.
Enhance power generation, transmission, and distribution for grid resiliency.
Explore the future of software-defined smart grids, including predictive maintenance of grid infrastructure, management of distributed energy resources, synthetic data generation of grid assets, outage scheduling, truck roll optimization, and utility contact center virtual assistants.
Learn from industry leaders using AI to optimize processes, reduce risk, and trim costs.
Image courtesy of BP.
See how BP achieved 35X runtime speedups by porting their production reverse time migration (RTM) code onto NVIDIA HGX™ A100 and leveraging the cuFFT library.
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Explore how Chevron utilized NVIDIA IndeX®, a 3D volumetric interactive visualization SDK, in Microsoft Azure to streamline analysis of core samples—in larger volumes and at higher resolution.
Stone Ridge Technology benchmarked their ECHELON reservoir simulation software on the NVIDIA Hopper GPU architecture, including the NVIDIA Grace Hopper Superchip, H100-NVL, and H100-PCIe. Learn how the company achieved up to 3.8x faster simulations with up to 25-million cell models.
Learn how global energy companies such as Siemens Energy are building industrial digital twins to support predictive maintenance at power plants and how that could save the energy industry an estimated $1.7 billion a year.
Image courtesy of Noteworthy AI.
Take a look at FirstEnergy’s onboard smart camera system—developed by Noteworthy AI and powered by the NVIDIA® Jetson™ edge AI platform—which automatically monitors millions of utility poles and tens of millions of grid devices for maintenance.
Shell has ongoing work with NVIDIA: more realistic 3D reservoir models (e.g., dipping reservoir) for CO2 storage, layered geology with horizontal and vertical heterogeneity, computationally efficient Fourier neural operator (FNO)-based networks dealing with larger input datasets and providing acceptable predictions over longer time windows (hundreds of years), and the capability to build next-generation digital twin models of deep earth for climate change scenario (CCS) applications in real time with uncertainty assessment.
— Pandu Devarakota, Principal Science Expert, Shell
We can examine the contribution of AI to the energy sector from three dimensions: energy forecasting, carbon capture, and predictive maintenance... AI algorithms are being used for energy forecasting, to predict energy demand, and to optimize economic value... AI can be used to reduce carbon emissions by analyzing data from multiple sources regarding weather, soil, and crop yield... to optimize our supply chain logistics and reduce our carbon footprint... AI can also help energy companies monitor the performance of their assets and equipment.
— Nayef Otaibi, Vice President and Chief Digital Officer, Saudi Aramco
We will continue to collect data, not just on how our wind turbines operate, but also for weather forecasting, site planning, and other areas to optimize wind turbine sites. We're exploring augmented reality and extended reality as wind turbines are complicated machines with many types of failure modes. It's imperative to make sure the wind turbines operate safely and service technicians know how to do service repairs in the right way.
— Lasse Lundberg Nowack, Vice President, Engineering Development Power Solutions, Vestas
By using synthetic data generation in NVIDIA Omniverse, our goal was to automatically create thousands of labeled photorealistic examples of various defects in grid assets. We are in the process of using real images and these synthetic images to train inspection models.
— Ankush Agarwal, Director of Advanced Analytics, Exelon
In Oregon, we are experiencing the impacts of climate change firsthand and recognize the urgent need for innovation at the grid edge as we transition to a clean energy future. Investing in new technologies for the grid is a key strategy for PGE to achieve its climate goals and provide customers with clean, affordable, and resilient energy.
— Ananth Sundaram, Senior Manager of Integrated Grid, Portland General Electric (PGE)
Systems powered by NVIDIA A100 80GB Tensor Core GPUs demonstrate superb performance uplifts compared to CPU performance running SLB’s INTERSECT high-resolution reservoir simulator.
Learn how Shearwater achieved 10x speed-up of Reverse Time Migration (RTM) and Kirchhoff algorithms, powered by NVIDIA GPUs, to lower total power consumption for compute-intensive subsurface workloads, improve energy efficiency, and reduce operating costs for oil and gas companies.
The Gigastack Project is a flagship, technologically advanced, large-scale renewable hydrogen project led by a consortium that includes ITM Power, Orsted, Phillips 66, and Element Energy. Worley assisted Phillips 66 with the front-end engineering of a 100 megawatt (MW) green hydrogen plant being integrated into the Phillips 66 Humber Refinery.
Geoscientists are using shallow deep learning-based algorithms with MATLAB on AWS and AI models to automatically recognize distinct geologic features in seismic images.
Utilidata announced that Portland General Electric will pilot Utilidata’s smart grid chip, a first-of-its-kind distributed AI platform powered by NVIDIA Jetson, in Oregon to support decarbonization goals.
Learn about the AI and HPC hardware, software, and networking solutions for energy companies.
The NVIDIA Grace Hopper™ Superchip is a breakthrough accelerated CPU designed from the ground up for giant-scale AI and HPC applications. The superchip will deliver up to 10X higher performance for applications running terabytes of data, enabling scientists and researchers to reach unprecedented solutions for the world’s most complex problems.
The latest iteration of NVIDIA DGX™ systems and the foundation of NVIDIA DGX SuperPOD™, DGX H100 is the AI powerhouse that’s accelerated by the groundbreaking performance of the NVIDIA H100 Tensor Core GPU.
NVIDIA DGX Cloud is a multi-node AI-training-as-a-service solution optimized for the unique demands of enterprise AI. It’s a combined software and infrastructure solution for AI training that includes a full-stack developer suite, leadership-class infrastructure, and concierge support, allowing businesses to get started immediately with predictable, all-in-one pricing.
With NVIDIA AI Enterprise, energy companies can speed up development of use case applications, such as reservoir simulation, seismic processing, and predictive maintenance. Learn how to get free, short-term access to NVIDIA AI Enterprise in curated labs through NVIDIA LaunchPad.
The NVIDIA HPC SDK includes the proven compilers, libraries and software tools essential to maximizing developer productivity and the performance and portability of HPC modeling and simulation applications.
NVIDIA Modulus is an open-source framework for building, training, and fine-tuning physics-informed machine learning (physics-ML) models with a simple Python interface. With Modulus, you can build models for enterprise-scale digital twin applications across multiple physics domains, from CFD to structural analysis to electromagnetics to climate science.
NVIDIA Omniverse is an extensible, open platform built for 3D virtual collaboration and real-time physically accurate simulation. Omniverse combined with NVIDIA Modulus, a framework for developing physics machine learning neural network models, enables digital twins for wind farms, power plants, electric grids, and someday Earth itself.
NVIDIA Jetson brings accelerated AI performance to the edge in a power-efficient and compact form factor. Together with the NVIDIA JetPack™ SDK and NVIDIA Isaac™ software for Robotics Operating System, these Jetson modules, including NVIDIA Jetson Orin Nano™, support a full range of edge AI and robotics applications.
NVIDIA NeMo™, part of the NVIDIA AI platform, is an end-to-end, cloud-native enterprise framework for building, customizing, and deploying generative AI models with billions of parameters. The NeMo framework provides an accelerated workflow for training with 3D parallelism techniques, several customization techniques, and optimized at-scale inference of large-scale models for language and image applications.
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To manage renewable energy at scale, NVIDIA and its ecosystem of partners are using AI to optimize solar and wind farms, simulate climate and weather, maintain power grids, advance carbon capture, and power fusion breakthroughs.
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Gain an understanding of the various building blocks of NVIDIA Modulus, the basics of physics-informed deep learning, and how the framework integrates with the overall Omniverse platform.
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