Case Study

Few computing challenges rival those found in geoscience and exploration workflows. Seismic imaging, full-waveform inversion and reservoir simulation are among the most computationally demanding workloads on Earth – on par with large-scale AI training and climate modelling. Even today, specialised geoscience systems occupy the second and third spots among the world’ s most powerful privately held supercomputers.

While industries like finance and life sciences have already embraced cloud computing for highperformance workloads, the energy sector’ s transition has been slower and more complex. The reasons are deeply technical.

1. RIGID LEGACY SYSTEMS

Most geoscience software was designed decades ago for fixed-size, on-premises supercomputers. These environments were optimised for tightly coupled parallelism and predictable hardware – not today’ s elastic cloud infrastructure.

Cloud pricing favours rapid, ondemand scaling, yet many legacy applications lack the flexibility to start, stop or resize efficiently – reducing both performance and cost efficiency.

2. INTOLERANCE TO INTERRUPTIONS

Public clouds offer discounted spot instances – temporary compute nodes that can be reclaimed without notice. While ideal for AI or analytics workloads, traditional seismic or reservoir codes cannot tolerate such interruptions. Losing one node can invalidate hours of computation.

To capture the cloud’ s economic advantage, fault tolerance and checkpointing must be engineered in, moving from static assumptions to resilient design.

3. THE DATA BOTTLENECK

A single seismic survey can hold tens to hundreds of terabytes of data that must be accessed by thousands of compute nodes simultaneously. This becomes difficult when file-based HPC systems meet object-based cloud storage.