Sharing Learnings: The Methodology, Optimisation and Benefits of Moving Subsurface Data to the Public Cloud

We detail the application of cloud technology to Chevron's seismic data repository, share the applicable learnings and highlight areas of workflow evolution that delivered value. The aim of the project, run as a piloted field trial, was to couple the transformative capability of public cloud services with subsurface data.

The learnings shared in this paper are designed to inform and assist other subsurface data custodians whether they are energy companies, national data repositories, service companies or academia.

Cloud technology enables us to reduce seismic data duplication to a single data version which can be accessed securely by all appropriate stakeholders. We discuss a highly automated, scalable data migration process which included seismic ingestion, machine learning and serverless architecture. This automated the data management process, progressing data from loading to global analysis in minutes.

The project has provided access to subsurface data anytime, anywhere and on any device, delivering a more accessible data environment at lower costs and connecting via an API to traditional workflows.

By approaching the subsurface data challenge in an innovative way, this project has provided multiple learnings to share and built a greater understanding of the value case for faster adoption of public cloud infrastructure.