An Interactive 5-Day Training Course
Open Subsurface Data Platforms & Cloud Geoscience Workflows
Cloud Platforms for Subsurface Data Work
Secure your spot now!
Duration
5 Days
Price
$ 3,000
Starting From
26 Jul 2027
Home Geology and Geoscience Open Subsurface Data Platforms & Cloud Geoscience Workflows
Open Subsurface Data Platforms & Cloud Geoscience Workflows
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Open Subsurface Data Platforms & Cloud Geoscience Workflows - GG-OSDU-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| GG-OSDU-PEA27 | 26 - 30 Jul 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
3000 |
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Open Subsurface Data Platforms & Cloud Geoscience Workflows
This training covers open subsurface data platforms and cloud based geoscience workflows. It works through the platform concept and the problem it addresses, data standards and schemas, platform architecture and services, data ingestion and curation, search and discovery, application integration, running computation in the cloud, security and access control, migration planning and the practical realities of platform adoption.
Description
Subsurface data has historically been held inside applications, each with its own database, format and access method, which makes integration a continuous manual effort and makes data reuse across applications difficult. Open data platform initiatives separate the data from the applications: data is stored once in a standard structure, and applications read and write it through defined interfaces. Combined with cloud computing, this changes how subsurface work can be organised, and the transition is substantial for any organisation attempting it.
This training covers the subject practically. The platform concept and the problem it addresses are established first, so that the architecture is understood in terms of what it fixes. Data standards and schemas follow, covering the data definitions that make platform storage possible. Platform architecture and services are then developed. Data ingestion and curation follow, covering the loading, mapping and quality control of existing data holdings. Search and discovery are then addressed. Application integration follows. Running computation in the cloud is then developed, covering the practical options and their cost. Security and access control follow. Migration planning is addressed. The training closes with the realities of adoption.
Separating data from applications is the central idea and the hardest part to implement. In the conventional arrangement, a well's logs live in the petrophysics application, its interpretation in the modelling package and its production history in another system, each with its own copy of the well header. In the platform arrangement there is one well record, one set of logs and one set of interpretations, and each application reads them. The benefit is obvious and the obstacle is that existing applications were not built this way, so adoption depends on connectors, on vendor support and on accepting a transitional period where both arrangements operate.
Data ingestion is where platform projects consume their budget. Loading decades of accumulated data into a standard schema requires mapping legacy field names to the standard, resolving the identifier inconsistencies that the old systems tolerated, deciding which of four versions of a log is authoritative, and handling the data that does not fit the schema at all. This work is unglamorous, it cannot be fully automated, and it is routinely underestimated by a large factor. It also produces most of the value, because the result is a clean inventory that did not previously exist.
Cloud computation changes what analyses are feasible. Running a thousand simulation realisations, processing a basin's worth of seismic, or training a model on all of a company's log data requires compute that is not available on a workstation and is expensive to own. Rented compute, available on demand and paid for by the hour, makes these practical. The constraints are that the data has to be in the cloud already for this to be efficient, that costs accumulate quickly without controls, and that some licensed applications cannot be run this way.
Finally, cloud cost control is an operational discipline that technical teams usually lack. Compute and storage are charged by use, and a forgotten virtual machine, a large data set in high-performance storage, or a workflow that transfers data repeatedly between regions can produce substantial bills with nothing to show. Tagging resources by project, setting budgets and alerts, shutting down idle resources and understanding the pricing model are all necessary, and teams that treat cloud as free capacity discover otherwise.
By the end of this training, participants will be able to:
- Explain the platform concept and the problems it addresses
- Apply subsurface data standards and schemas
- Describe platform architecture and its services
- Plan and execute data ingestion and curation
- Map legacy data to standard schemas
- Use search and discovery capabilities effectively
- Integrate applications with platform data
- Run geoscience computation in cloud environments
- Apply security, access control and data sovereignty requirements
- Plan migration and manage cloud cost
The training combines concept development with practical work on platform environments. Participants map legacy data sets to standard schemas, ingest and curate data, and run search and computation workflows. Migration plans are built for real data holdings with effort and cost estimated. Cloud cost scenarios are examined and control measures designed.
Organisations sending participants to this training will:
- Plan platform adoption with realistic effort estimates
- Map and curate legacy data to standards
- Integrate applications with shared data
- Use cloud computation for previously infeasible analyses
- Control cloud cost from the outset
- Reduce the manual effort of data integration
Participants will:
- Understand platform architecture and standards
- Ingest and curate subsurface data
- Integrate applications with platform data
- Run cloud based geoscience computation
- Apply access control and security requirements
- Contribute to platform adoption projects
- Data managers and data architects
- Geoscientists working with platform data
- Information technology staff supporting subsurface systems
- Digital transformation staff
- Technical staff planning platform migration
- Data scientists accessing subsurface data
- Managers responsible for platform decisions
Module 1 - The Platform Concept
- Problems with application centred data
- Duplication and inconsistency
- Integration effort in current workflows
- Separating data from applications
- Open platform initiatives
- OSDU and its scope
- Vendor platform offerings
- Open against proprietary approaches
- Benefits claimed and benefits realised
- Adoption status across the industry
- Transitional coexistence of approaches
- What a platform does not solve
Module 2 - Data Standards and Schemas
- Role of standards in platforms
- Energistics standards family
- WITSML, PRODML and RESQML
- Schema concepts and structure
- Well and wellbore schemas
- Log and curve schemas
- Seismic data schemas
- Reservoir model schemas
- Reference data and code lists
- Extending schemas for local needs
- Schema versioning
- Data that does not fit the schema
Module 3 - Platform Architecture
- Architecture layers and components
- Storage services and data lakes
- Object and file storage
- Metadata and index services
- Search services
- Entitlement and identity services
- Workflow and orchestration services
- Application programming interfaces
- Ingestion services
- Delivery and consumption services
- Deployment options and providers
- Hybrid and on-premise options
Module 4 - Ingestion and Curation
- Data inventory and prioritisation
- Source system assessment
- Legacy format handling
- Field mapping to schema
- Identifier reconciliation
- Duplicate resolution
- Version and authority decisions
- Quality control during ingestion
- Enrichment and metadata completion
- Automated against manual curation
- Effort estimation for ingestion
- Incremental ingestion strategies
Module 5 - Search and Discovery
- Search capabilities in platforms
- Metadata driven search
- Spatial search
- Attribute and range queries
- Full text search of documents
- Faceted search and filtering
- Result sets and their delivery
- Data lineage and provenance tracking
- Finding all data for a well
- Finding data for an area or play
- Search quality and metadata completeness
- Discovery as the main user benefit
Module 6 - Application Integration
- Application connector concepts
- Reading platform data from applications
- Writing results back to the platform
- Vendor connector availability
- Custom integration development
- Programmatic access through interfaces
- Scripting against platform data
- Notebook based access
- Data caching and local copies
- Round trip integrity
- Workflow spanning multiple applications
- Integration testing
Module 7 - Cloud Computation
- Cloud compute models
- Virtual machines and containers
- Serverless and managed services
- Scaling and parallel execution
- Simulation ensembles in the cloud
- Seismic processing in the cloud
- Machine learning training
- Storage classes and their cost
- Data transfer and egress charges
- Cost estimation and budgeting
- Cost control and monitoring
- Software licensing in cloud environments
Module 8 - Security, Migration and Adoption
- Identity and access management
- Entitlements and data level security
- Partner and third party access
- Confidentiality and regulatory restrictions
- Data sovereignty and residency requirements
- Encryption and key management
- Audit and compliance
- Migration planning and phasing
- Pilot scope selection
- Change management and user adoption
- Realistic benefit expectations
- Lessons from platform projects
Upon successful completion of this training course, delegates will be awarded an official Certificate of Completion issued by the Petroleum Engineers Association (PEA), an ISO 9001:2015 certified training organization. The certificate carries 10 Credits and formally records the total learning hours completed.
Each certificate is signed by the Course Facilitator and the CEO of the Petroleum Engineers Association, and serves as verifiable proof of professional training that delegates can present to employers and professional bodies worldwide.
Frequently Asked Questions
All course bookings made through PEA are strictly non-refundable. By registering for a course, you acknowledge and accept that all fees are payable in full and are not subject to refund under any circumstances, including changes in personal or professional commitments or partial attendance.
PEA reserves the right to make reasonable adjustments to course content, trainers, or schedules where necessary, without entitling delegates to a refund. Comprehensive details of each course — including objectives, target audience, and content — are clearly outlined before enrolment, and it is the responsibility of the delegate to ensure the course's suitability prior to booking.
For any inquiries related to cancellations or bookings, please contact our support team, who will be happy to assist you.
