An Interactive 5-Day Training Course
Geoscience Data Management & Standards
Managing Geoscience Data to Proper Standards
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Duration
5 Days
Price
$ 3,000
Starting From
05 Jul 2027
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Geoscience Data Management & Standards
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Geoscience Data Management & Standards - GG-DATA-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| GG-DATA-PEA27 | 05 - 09 Jul 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
3000 |
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Geoscience Data Management & Standards
This training covers geoscience data management and standards. It works through data types and their life cycles, master data and reference data, naming conventions and identifiers, metadata requirements, data quality management, standards and formats, database and repository architecture, governance and ownership, physical data and sample management and the business case for data management.
Description
Subsurface data is a major asset that is routinely managed badly. Wells appear under several names, the same log exists in four versions with no indication which is current, coordinates are stored without their reference system, interpretations are saved on personal drives, and the time spent locating and verifying data before any technical work begins is a substantial fraction of the technical workload. These are solvable problems with established solutions, and the obstacle is usually organisational rather than technical.
This training covers data management as it applies to subsurface data. Data types and their life cycles are established first, covering the stages from acquisition to archive and the handling each requires. Master data and reference data follow, covering the authoritative records that everything else refers to. Naming conventions and identifiers are then developed, since inconsistent well naming is the single most common data problem in the industry. Metadata requirements follow, covering what has to be recorded for data to remain usable. Data quality management is then addressed. Standards and formats follow. Database and repository architecture are then developed. Governance and ownership are addressed. Physical data and sample management follow. The training closes with the business case.
A unique well identifier is the foundation and most organisations lack one. The same well is known by its operator name, its regulatory number, its internal project name, an abbreviation in a spreadsheet and a different spelling in a legacy database. Without a single authoritative identifier that every system refers to, data cannot be reliably joined, a complete data inventory for a well cannot be produced, and every integration exercise begins with manual reconciliation. Establishing and enforcing unique identifiers is the highest-value data management action available.
Coordinates without their reference system are not coordinates. A position stored as a pair of numbers, with no record of the projection, the datum and the units, can be wrong by hundreds of metres when interpreted under the wrong assumption, and the error is invisible because the numbers look reasonable. Well positions, seismic navigation and map data all suffer from this, and the resulting misties are a common cause of interpretation error. Recording the coordinate reference system with every spatial data item is a basic requirement that is widely neglected.
Metadata determines whether data survives the person who created it. A grid file named final_v3 tells the next user nothing about what it represents, who made it, from what inputs, under what assumptions or whether it was superseded. The same file with a recorded description, author, date, source data and status remains usable indefinitely. The effort is small at creation and impossible to reconstruct later, which is why metadata capture has to be part of the workflow rather than a subsequent tidying exercise.
Finally, the business case for data management is made in time saved, and the figures are substantial. Studies across the industry have repeatedly found that technical staff spend a large fraction of their time finding, checking and reformatting data rather than interpreting it. Reducing that fraction by even a few percentage points across a technical workforce pays for a data management function several times over. Making this argument quantitatively, with measurement from the organisation's own staff, is more persuasive than an appeal to good practice.
By the end of this training, participants will be able to:
- Describe subsurface data types and their life cycles
- Define master and reference data and their management
- Establish naming conventions and unique identifiers
- Specify metadata requirements for each data type
- Manage coordinate reference systems correctly
- Design and operate data quality management processes
- Apply industry data standards and formats
- Assess database and repository architecture options
- Establish data governance, ownership and stewardship
- Make the business case for data management investment
The training works on real data management problems. Participants audit supplied data sets for identifier, metadata and coordinate problems and quantify the effect, design naming conventions and metadata standards, and build governance proposals. A business case is constructed from measured time spent on data handling in participants' own organisations.
Organisations sending participants to this training will:
- Establish unique identifiers and consistent naming
- Record metadata that keeps data usable
- Manage coordinate reference systems correctly
- Reduce time spent finding and verifying data
- Establish governance and ownership of data
- Justify data management investment with evidence
Participants will:
- Audit data for management problems
- Design naming and metadata standards
- Manage spatial reference systems correctly
- Operate data quality processes
- Establish governance arrangements
- Make the case for data management
- Data managers and data coordinators
- Geoscientists responsible for team data
- Petrophysicists managing log data
- Information technology staff supporting subsurface systems
- Subsurface team leaders
- Technical staff specifying data systems
- Managers responsible for data investment
Module 1 - Data Types and Life Cycles
- Subsurface data categories
- Well and borehole data
- Log and petrophysical data
- Core and sample data
- Seismic and geophysical data
- Production and pressure data
- Interpretation and derived data
- Spatial and map data
- Document and report data
- Life cycle stages from acquisition to archive
- Handling requirements at each stage
- Retention and disposal policy
Module 2 - Master and Reference Data
- Master data concept
- Authoritative source definition
- Well master data content
- Field and asset master data
- Reference data and lookup lists
- Lithology and facies code lists
- Stratigraphic nomenclature control
- Unit of measure management
- Single source of truth principle
- Synchronisation between systems
- Master data maintenance processes
- Ownership of master data
Module 3 - Naming and Identifiers
- Well naming problems and their cost
- Unique well identifier schemes
- Regulatory identifiers
- Internal identifier design
- Alias and cross-reference tables
- Wellbore and completion identifiers
- Seismic survey naming
- Horizon and marker naming conventions
- File naming conventions
- Version and status indicators
- Enforcing conventions in practice
- Retrofitting identifiers to legacy data
Module 4 - Metadata
- Metadata purpose and content
- Descriptive metadata
- Provenance and lineage
- Author, date and version
- Source data references
- Processing and assumption records
- Status: draft, approved, superseded
- Coordinate reference system recording
- Datum and elevation reference
- Units and their recording
- Metadata capture in workflows
- Metadata standards and schemas
Module 5 - Spatial Data and Coordinates
- Coordinate reference system components
- Geographic and projected systems
- Datums and their differences
- Common projections in use
- Transformation between systems
- Transformation parameter selection
- Errors from wrong assumptions
- Well position and deviation data
- Seismic navigation data
- Elevation and depth references
- True vertical depth conventions
- Spatial data quality control
Module 6 - Data Quality
- Data quality dimensions
- Completeness, accuracy and consistency
- Quality rules and their definition
- Automated validation
- Range and consistency checks
- Cross-data-type consistency
- Duplicate detection
- Quality measurement and reporting
- Remediation processes and prioritisation
- Preventing quality problems at entry
- Quality responsibilities
- Quality improvement programmes
Module 7 - Standards, Systems and Physical Data
- Industry data standards
- Energistics standards and their scope
- WITSML, PRODML and RESQML
- OSDU data platform concepts
- Log data formats and standards
- Database architecture options
- Repository and project data separation
- Application data stores
- Integration and interoperability
- Core and sample store management
- Physical sample tracking
- Document and report management
Module 8 - Governance and the Business Case
- Data governance structures
- Data ownership definition
- Data stewardship roles
- Policies and standards documents
- Compliance and audit
- Change control for data
- Access control and security
- Regulatory and partner data obligations
- Measuring time spent on data handling
- Quantifying the cost of poor data
- Building the business case
- Implementing improvement incrementally
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.
