Accelerate subsurface workloads with AI, high-performance computing (HPC) and visualization.
Oil and gas enterprises are becoming integrated energy companies, using improved processes to better explore, produce, transport, and distribute fuels to consumers around the world. Global initiatives for decarbonization are accelerating the transition from fossil fuels to alternative fuels with a smaller carbon footprint and scaling renewable energy, such as wind, solar, nuclear, hydroelectric, green hydrogen and geothermal.
SLB and NVIDIA are collaborating to build and optimize generative AI models for the energy industry using NVIDIA NeMo™ and NIM™ microservices, including subsurface exploration, production operations, and data management.
Petrobras accelerated SolverBR, a linear equations solver for reservoir simulation, using the NVIDIA Grace™ CPU. This included 4.5X faster time-to-solution, 4.3X greater energy efficiency, and 1.5X higher scalability
Learn from energy companies using AI to improve energy production and reduce operating costs.
Learn how Saudi Aramco built an AI assistant to enhance seismic data processing—compiled with NVIDIA® TensorRT™-LLM, deployed using NVIDIA Triton™ Inference Server, and fine-tuned with NVIDIA NeMo Guardrails.
Saudi Aramco
Learn how Petrobras built SolverBR—a specialized linear solver powered by the Arm®-based NVIDIA Grace CPU—and achieved up to a 60 percent speedup in reservoir simulations.
Shell is revolutionizing CO2 storage modeling by accelerating simulations 100,00X with NVIDIA Modulus, an open-source framework for building, training, and fine-tuning physics-machine learning models.
Shell
As the world moves toward a more sustainable future, global leaders in energy are accelerating their key HPC and AI workloads with NVIDIA’s full-stack platform. The result? Some of the most powerful, efficient supercomputers in the industry—including those from Eni, ExxonMobil, Petrobras, Saudi Aramco, Total, and the U.S. Department of Energy—are built on NVIDIA technology and continually rank near the top of the TOP500 and Green500 lists.
Our collaboration with NVIDIA provides great value because NVIDIA brings expertise in AI and a powerful HPC platform to conduct these studies. Conversely, Shell injects realism and provides the energy experience that provides great value to NVIDIA.
– Detlef Hohl, Chief Scientist of Computation and Data Science, Shell
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 FNO-based networks dealing larger input datasets and providing acceptable predictions over longer time windows (hundreds of years), and next-generation digital twin models of deep earth for carbon capture and storage (CCS) applications in real time with uncertainty assessment.
– Pandu Devarakota, Principal Science Expert, Shell
In 2022, over 70 percent of computing resources at BP’s Center for High-Performance Computing were used by reverse time migration (RTM)-related applications. In RTM benchmarking and validation tests, we found that one NVIDIA HGX A100 with no compression achieved up to a 35X peak shot runtime speedup compared to our in-house CLXAP cluster.
– Muhong Zhou, Senior HPC Software Developer, BP
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
The exploration and production (E&P) industry generates a vast amount of data, including well logs, seismic data, production data, drilling data, and other sources. Natural language processing (NLP) and large language models (LLMs) have provided the ability to integrate sources with descriptive, diagnostic, prescriptive, and predictive insights that enhance return on investment. In reservoir modeling, NLP and LLMs improve integrating sources from well logs, seismic data, and production data—bringing critical understanding to the subsurface that’s significant to our operations.
– Saud Zakwani, Head of Data Science, Petroleum Development Oman
AI can help in site selection and allocation by studying the physics, geology, and metrology of different locations to optimize solar plants or wind farms and make them more cost-effective and productive. Once facilities are deployed, we can look at AI to balance the grid and identify production capacity predicted from renewable energy. AI can help optimize a large installation of batteries, such as charging and discharging or maintenance issues.
– Yehia Khoja, Ministry of Energy, Head of AI and Business Development, Kingdom of Saudi Arabia
Fraunhofer IML
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.
Shearwater is expanding its collaboration with NVIDIA to innovate subsurface processing and imaging with higher performance-per-watt, energy efficiency and fewer emissions on the NVIDIA Grace Hopper™ Superchip.
SoftServe is using physics-informed neural networks powered by NVIDIA Modulus to find an optimal flow rate for virtual metering in electrical submersible pump (ESP) wells—improving equipment lifecycle and profitability.
Learn about the AI and HPC hardware and software for oil and gas.
NVIDIA DGX H100 expands the frontiers of business innovation and optimization. The latest iteration of NVIDIA’s legendary 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 SuperPOD is an AI data center infrastructure platform that enables IT to deliver performance without compromise for every user and workload. DGX SuperPOD offers leadership-class accelerated infrastructure and agile, scalable performance for the most challenging AI and HPC workloads, with industry-proven results.
NVIDIA DGX Cloud is a multi-node AI-training-as-a-service solution optimized for the unique demands of enterprise AI. Access NVIDIA DGX Cloud to experience a combined software and infrastructure solution for AI training that includes a full-stack AI developer suite, leadership-class infrastructure, and concierge support.
The NVIDIA Grace™ Hopper™ Superchip is a breakthrough accelerated CPU designed from the ground up for giant-scale AI and HPC applications. The superchip delivers 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 NVIDIA HPC Software Development Kit (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.
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 access to NVIDIA AI Enterprise with free curated labs to start testing.
NVIDIA vGPU software enables powerful GPU performance for workloads ranging from graphics-rich virtual workstations to data science and AI, enabling IT to leverage the management and security benefits of virtualization, as well as the performance of NVIDIA GPUs required for modern workloads.
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. Energy companies are exploring large language models in sentiment analysis for oil price prediction, as well as integrating sources from well logs, seismic data, and production data to generate real-time insights.
NVIDIA Metropolis features GPU-accelerated SDKs and developer tools that help developers optimally build, deploy, and scale AI-enabled video analytics and IoT applications—from the edge to the cloud.
Use NVIDIA RAPIDS™ to apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, cuML’s k-nearest neighbor (KNN), DBSCAN, and logistic regression, for data analysis at scale.
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 NVIDIA Omniverse™ platform.
Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for HPC application development.
Learn how to use NVIDIA Base Command™ Platform to accelerate containerized AI training workloads, identify the tools necessary to build an AI center of excellence, and get the basics of working with, modifying, and running Docker containers from NVIDIA NGC™.
Transform your AI workloads with the NVIDIA H100 Tensor Core GPU, featuring Multi-Instance GPU (MIG), the Transformer Engine, and NVIDIA AI Enterprise.
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