Subsurface Oil & Gas Operations, Optimized With 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.

• Review how GPU-accelerated HPC and AI are transforming energy workflows.

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.

• See how NVIDIA Omniverse creates digital twins for energy pipelines.

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.

• Read about accelerating reservoir simulation workflows with PhysicsNeMo.

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.

• Explore AI-driven seismic processing and reservoir modeling techniques.

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.

• Review seismic processing and imaging technologies.

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.