By leveraging AI-enhanced 3D volumetric visualization frameworks and HPC, energy companies are streamlining the core-sample analysis workflow, while interacting with larger volumes in higher resolution. This includes forward waveform inversion (FWI) and reverse time migration (RTM)—critical seismic imaging workflows.
Subsurface Oil & Gas Operations, Optimized With AI
Leveraging AI workloads with high-performance computing and visualization to transform workflows.
Introduction
How to Accelerate Resource Discovery, While Reducing Environmental Impact
Oil and gas enterprises are transforming into integrated energy companies. Using improved processes, including GPU-powered workloads, they are reducing capital spending by shortening time to site detection and enabling better exploration, production, transport, and distribution of fuels to consumers around the world. Additionally, 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.
Use Cases
AI Use Cases for Subsurface Operations
Seismic Image Processing
Imaging subsurface geology.
Reservoir Simulation
Predicting and analyzing fluid behavior.
Geoscience and Engineering AI Agents
Optimizing site operations autonomously.
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News and Events
Oil and Gas News
Connecting Industries Through Energy Innovation
Cross-Industry Connections in Subsurface Energy
FAQs About Subsurface AI
GPU-powered workloads allow energy companies to perform high-performance computing (HPC) and 3D volumetric visualization, which streamlines workflows like core-sample analysis and complex seismic imaging. This enables deeper insights from larger datasets, resulting in reduced capital spending and improved exploration accuracy.
Generative AI models are trained on specialized datasets such as seismic surveys, well logs, grid sensor data, power plant data, and smart meter readings to reduce operating costs and enhance productivity. These models help energy organizations convert vast operational data into actionable insights for decision-making.
- Learn about generative AI models for the energy industry.
NVIDIA NeMo is a framework for building, customizing, and deploying generative AI models. In the energy sector, it is used to optimize domain-specific AI models for subsurface exploration, production operations, and efficient data management.
- Explore the capabilities of NVIDIA NeMo for custom AI model development.
NVIDIA Omniverse is a platform that enables the creation and operation of OpenUSD-based 3D workflows and digital twins. In energy, it is used by software makers to develop realistic simulations and digital replicas of subsurface environments and infrastructure.
PhysicsNeMo is an open-source framework for building and training physics-ML models that are used for full-field proxy generation. It provides results 10x–100x faster than traditional forward simulations while maintaining reasonable accuracy compared to full-physics numerical solutions.
The Fourier Neural Operator (FNO) is a specialized network architecture used to handle large input datasets for complex geological modeling. It allows for computationally efficient predictions over long time windows, which is essential for managing the long-term behavior of underground reservoirs.
HPC enables the processing of seismic data at a massive scale, allowing for advanced techniques such as Forward Waveform Inversion (FWI) and Reverse Time Migration (RTM). These computational methods allow geoscientists to visualize subsurface geology with higher resolution and speed.
- Review technical details on accelerating HPC in energy with NVIDIA samples.
Agentic AI utilizes full-stack AI frameworks to create specialized, autonomous agents that manage specific tasks within the energy pipeline. These agents provide domain-specific insights that speed up production timelines by automating complex operational decisions.
Discover how autonomous AI agents are being deployed in energy sectors.
FWI (Full Waveform Inversion) and RTM (Reverse Time Migration) are critical seismic imaging workflows used to create accurate models of subsurface geology. They require massive computational power, which is optimized by AI and HPC to provide clearer images of reservoir structures.
An OpenUSD-based digital twin allows for the interchange of high-fidelity 3D data across different software tools, facilitating a collaborative and realistic simulation environment. This ensures that subsurface models maintain accuracy and visual quality throughout the engineering and simulation process.
- Explore the benefits of OpenUSD-based digital twins.
Resources
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