Normalized claim
Time: 2-3 days decrease
Cut survey processing turnaround time from approximately 20 days to 2-3 days.
GCPEvidence: Medium50/100
PGS: Taking seismic intelligence to new depths (Google Cloud GKE HPC)
Use case typeCloud migrationUpdated Jun 13, 2026
PGS is an integrated marine geophysics company based in Norway, with offices in 14 countries. It maps the ocean subsurface to support offshore renewables and carbon storage. The company expanded its high-performance compute environment on Google Cloud, moving seismic imaging workloads from an initial lift-and-shift approach to a cloud-native architecture centered on Google Kubernetes Engine.
- Organization
- PGS
- Industry
- Energy & Utilities
- Location
- Norway
- Published
- June 2026
Reported outcomes
2-3 days
timeTime & speed
80%quantified impact
Strategic outcomes
Speed & agilityReduced seismic survey turnaround timeCost efficiencyReduced capital expenditure through pay-per-useScale & capacityShut down two data centers
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 80%
Moved 80% of HPC workload to Google Cloud and shut down two main data centers.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- PGS
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud Customer Stories
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 3
- 1High-performance computing
- 2Cloud migration
- 3Data processing optimization
Expand high-performance compute capacity for seismic imaging while reducing idle capacity, capital expenditure, and turnaround time for seismic survey processing.
- PGS migrated HPC workloads to Google Cloud, first using a lift-and-shift approach and then a more cloud-native setup on Google Kubernetes Engine as the HPC scheduler.
- The company used Google Cloud Storage for petabyte-scale data movement and integrated BigQuery and other cloud services for analytics and enterprise workloads.
- PGS used a burst model to scale compute resources up and down on demand and later adapted its most-used algorithms to the cloud.
- Cut survey processing turnaround time from approximately 20 days to 2-3 days.
- Increased peak compute to 1.2 million vCPU.
- Moved 80% of HPC workload to Google Cloud and shut down two main data centers.
- Reduced capital expenditure by avoiding idle servers and paying only for what is used.
- Expanded compute capacity to a cloud-based supercomputer described as being among the top 25 supercomputers in existence.
Architecture
PGS built a cloud HPC environment on Google Cloud for seismic imaging. Google Kubernetes Engine acts as the core HPC scheduler, with Google Cloud Storage supporting large-scale data movement and BigQuery used for analytics and business intelligence. The company initially lifted and shifted workloads, then moved to a cloud-native burst model so compute could scale up for demanding survey processing and disappear automatically when work completed.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 6, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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