Democratize AI Infrastructure for the Oil, Gas and Energy Industry

The Challenge
Energy operators are deploying AI across core exploration, production, and operational optimization workflows. These workloads are computationally intensive and data-heavy, often involving multi-terabyte datasets and GPU-accelerated processing cycles.

However, most infrastructure environments in the sector were built for control systems, transactional data, and traditional HPC clusters—not dynamic, GPU-dense AI pipelines.

As a result:
• Models are constrained by I/O bottlenecks rather than algorithmic limits
• GPUs are provisioned in fixed clusters, leading to stranded capacity or overbuild
• Large datasets must be repeatedly staged between storage tiers
• AI workflows are isolated from operational systems, requiring manual integration

The challenge is not model innovation. It is enabling sustained, high-throughput AI workloads within industrial infrastructure that was never architected for them.

Corespan’s Approach
Corespan delivers an AI infrastructure fabric purpose-built for brownfield energy operations. Instead of bolting models onto disconnected systems, Corespan delivers a unified, high-performance AI infrastructure architecture that can be deployed in a data center, modular rack, or compact on-site environment. AI workloads are built once and executed on a consistent infrastructure foundation, regardless of which business unit or operational domain uses them.

Key Elements
Optical PCIe Fabric and Composable HPC:
Corespan extends PCIe over optical links to pool GPUs, accelerators, and high-performance NVMe storage across chassis and racks, enabling true disaggregation of compute and memory across the data center and selected field locations. A software-defined control plane dynamically assembles GPUs, CPU nodes, and NVMe scratch pads per job, seismic processing, reservoir simulation, refinery optimization, or real-time grid analytics then releases them when complete. This maximizes utilization, avoids stranded capacity at remote sites, and keeps latency low enough for time-sensitive inference and control.

NVMe Scratch Pads for Data-Intensive AI:
High-speed NVMe SSDs are provisioned as a high-speed staging tier sitting close to compute to temporarily hold active datasets and model artifacts (commonly referred to as a scratch pad). It absorbs burst I/O and prevents slower storage systems from stalling jobs, improving overall throughput and compute utilization. Allocation and cleanup are automated so teams do not manage storage details.

Legacy Interoperability, Not Rip-and-Replace:
Corespan enables next-generation GPUs and accelerators to operate alongside existing server infrastructure. Organizations can introduce high-performance AI compute without replacing installed CPU platforms or rebuilding data center environments.

Instead of forcing a full hardware refresh cycle, Corespan decouples accelerators from fixed server configurations. Modern GPUs are pooled and dynamically attached to legacy or current-generation systems as needed.
This preserves prior infrastructure investments while enabling immediate access to state-of-the-art AI performance.

Business Impact
Increase Production and Recovery
Run higher-fidelity production optimization models more frequently by leveraging pooled GPU resources and high-speed staging storage. Shorter compute cycles and reduced queuing accelerate decision timelines and improve asset performance.

Reduce unplanned downtime and maintenance cost
Standardize predictive maintenance models across rotating equipment, pipelines, and power assets, using edge inference and centralized learning to catch issues earlier and extend asset life.

Improve energy and emissions performance
Combine process data, sensor streams, and market signals to optimize energy use, reduce waste, and support regulatory and emissions reporting while maintaining throughput.

Conclusion
In the energy sector, AI value depends on infrastructure that respects the realities of brownfield assets, remote operations, and regulatory constraints. Fragmented systems and distant cloud endpoints cannot, on their own, support reliable AI infrastructure for critical operations. Corespan provides an integrated PCIe-over-optics, NVMe-accelerated, software-defined foundation that connects legacy operations with modern AI capabilities, turning operational data into timely, actionable intelligence at enterprise scale.