We managed to achieve significant progress in areas such as:
- A new cloud-native platform for seismic imaging – PGS Eos
- Managing a cloud-enabled fleet – PGS Proteus - predictive maintenance, optimized vessel speeds, and productivity improvements from big data analytics
- Accessing and using MultiClient data in the cloud using new business models – PGS Solis
PGS Solis — MultiClient Data in the Cloud
Access and act on data in the cloud
Flexible subscription models
Data management as a service (DMaaS)
PGS Solis is a data management platform for subsurface information that will offer instant access to MutliClient data to subscribers, as well as data management as a service. It will be integrated in our pioneering strategic partnership with TGS and CGG, as part of a shared marketplace for subsurface MultiClient data.
We expect the momentum to increase further in 2021 as we continue our digital transformation journey and ncreasingly take advantage of the more effective and productive ways of working.
PGS Proteus — Cloud Enabled Fleet
New insights from production-scale data analytics
View, analyze and act on worldwide operations
Collect, connect, operationalize & scale
PGS Proteus harnesses the power of big data to optimize vessel operations. Various focus areas target energy efficiency, vessel speed, equipment maintenance of streamers and eBirds, and improved HSEQ for small boat operations and use of personal protection equipment. All use-cases have delivered measurable results and we have started to fully implement them across our active fleet. We will continue to harvest and evolve these productivity improvements.
PGS Eos — Imaging in the Cloud
Managing finite resources
Faster when it counts
Through our cloud-enabled PGS Eos platform, we look forward to fully utilizing the benefits of the cloud to deliver reliable and timely results to our clients. This initiative has potential to access unlimited compute capacity and application of the latest technology. We have begun our first commercial imaging project in the cloud.
We have also started leveraging machine learning and artificial intelligence in seismic imaging, to improve our velocity model building, allowing for faster convergence to the final velocity model. What used to take months, now takes weeks. Further, a proof-of-concept project by our Imaging teams to identify patterns in historic processing sequences shows that seismic processing parameter extraction may produce equivalent seismic quality by using historical trends, thereby minimizing testing and accelerating data delivery.