Postgraduate Petroleum Data Management 2016-2017

 Data Sources, Types and Formats:

• Spatial Data

• Culture
• Boundaries/Licenses/Leases
• Licence history
• Bathymetry/Topography
• Surface images
• Coordinate Systems
• Subsea data (e.g. infrastructure and pipelines)

• Geophysical Data

• 2D/3D Navigation
• Gravity & Magnetics
• 3D Outlines
• 2D/3D Seismic trace
• 4D Seismic Time-Lapse data
• Acquisition and Processing parameters
• Velocities

• Interpretation projects

• Seismic Interpretation and Interpretation Studies
• Horizon Time Grid and Depth Grid
• Geologic and Simulation Models
• Reserve calculations
• Prospects

• Well Data

• Stratigraphic Columns
• Identity: Headers, Directional
• Drilling: Planning, Drilling, Completion, Events, Drilling / W-O treatment
• Formation: Core description, Geochemistry, Surface picks, Intervals, Pressures
• Logs: Curves, Petrophysical parameters, Zoned properties, Checkshots, VSP, Synthetic seismograms

• Production Data

• Configuration: External network, Network, Surface, Sub-surface
• Regular: Measured volumes, Pump data, Other measurements, Operational, Allocated volumes
• Occasional: Planned events, Unplanned events, Samples, Well tests

• Reservoir Data

• Porosity
• Permeability
• Fluids
• “Problems”
• Well tests (e.g. Drill stem test, Formation Integrity test)

• Indexes

• Physical assets
• Scanned documents
• Electronic Documents

• The value of these different data types

• Data sourcing workflows

• Tools and techniques for

• Using, loading, altering, and converting different data types and data sets
• Data storage
• Data archiving
• Cataloguing data for effective search and retrieval
• All of the above for Big Data

• Introduction to regulatory reporting and data sharing between partners

• Introduction to associated standards

• Introduction to associated IT concepts

• Introduction to Data Management Architecture and Data Management Models

• Definitions of reference, corporate and project data
• The role and value of reference data
• Applying, identifying and reporting reference data
• Checking the compliance of corporate data to reference data
• The relation between corporate data and project data
• Project data management
• Analysis to ensure corporate data is properly managed over its lifecycle
• Industry standards for reference data
• Information Architecture and Categorization
• Indexing, Tagging and Cataloguing, and the use of Ontologies and Folksonomies for this
• Data Tracking and Data Provenance
• Physical Data Management (including cores, samples, paper, tapes and other media)
• Cross business sharing of data, and issues arising (e.g. regarding synchronizing reference, corporate and project data across multiple locations; big data and data duplication; combining subsurface, surface data and activity).
• Purpose, business drivers, and operating considerations of National Petroleum Data Repositories, building on the regulatory link in PD50X1. The short term (encouraging access and promoting development) and long term (retention of scientific knowledge) objectives for these, how they are funded, and examples of good practice.

Managing data support services for various stakeholder groups

o Service design and implementation

o Monitoring and tracking data and service requests

o Demand and resource management

o Service level agreements and service charters

o Service reporting and service reporting policies  

o Projects as a way of changing/improving services or introducing new services

o Measuring project impact (including qualitative techniques)

o Change management (e.g. change management processes for data standards, reference data, security classification)

o Risk management

o Liaising with other departments and third parties

o Influencing stakeholders (e.g. related to the need for data quality, accessibility, traceability, archiving and retention processes and procedures)

o Project planning and control, tracking by stages, reporting by exception

o Producing a business case, and estimating value and costs of data and data management process improvements

Project management

o Improving services through change management

o Service measurement (KPIs) and service measurement policies

o Managing incidents and complaints

o Sourcing external data, and monitoring requests for external data for data rights management and invoicing

o Managing (virtual) teams

o Front office / Back office services

• Data Governance Framework

• Data Vision and Mission

• Data Flows, Data Life Cycle and the Value Chain

• E&P business workflows and how data governance impacts these

• Data Management Organization Models and Structures

• Data Roles and Responsibilities / Ownership

• Data Policies, Strategies, Standards, and Procedures:

The role of, value of and need for data policies, strategies, standards and procedures

The corporate data strategy and its relationship to the business strategy

Definition, implementation and review of data policies, strategies and standard operating procedures.

• Policies and procedures for:

enabling exploitation of information assets

data capture, loading, recording, retention, destruction and publication

data distribution and sharing

• Lifecycle of policies

Standards for data capture, loading, recording, and transfer

Strategies for service management and service delivery

Auditing and assessing the business’ data policies and procedures, and adherence to these. 

• The relation between Data Governance and Data Quality, Security, Entitlements and Obligations, and the use of standards to achieve compliance and security requirements
• Data Management Maturity and Impact o Assessing the maturity and measuring the effectiveness of data management

Monitoring metrics for good data management

Assessing and communicating the impact of current data management on the effectiveness of the asset

Presenting a case for improvements in data management

• The value of data quality and data quality management, and business consequences of poor data quality (lost value, increased costs, increased risk, reputational damage)
• Inherent uncertainty in data values and the implications for data use
• The contextual nature of data quality management which also depends on how data is used and context of use (not all applications require great precision or completeness, which incur costs)
• The difference between standards and rules deriving from the nature of the data, and those deriving from the business purpose the data meets; how standards and rules may differ by country and context
• Addressing data quality issues (e.g. through data clean-up projects and application of data analytics) • Auditing and assessing the business’ data quality processes and adherence to standards and business rules
• The relation between data quality management and data governance.
• Using business rules for loading and cleansing different data types and data sets
• Identifying, monitoring for, handling, and reporting data quality issues
• The role of, value of and need for data quality standards, business rules, policies, and procedures, and how these are used to lead compliance activities
• Tracking data uncertainty and quality; the importance of data users documenting assumptions and precision
• Common data quality issues (including issues related to data completeness, consistency and precision) and how to deal with these

• The Data into Information Pathway (DIKW pyramid)
• Tools for Data Handling, Processing, Analysis, and Interpretation
• Tools for Data Quality
• GIS and Spatial Data Management techniques and tools:

Digital Mapping and Map Design

Datums and Map Projections

2D and 3D geovisualisation

Spatial queries

Analysing spatial data

WebGIS & Map Services

Recording, checking and assuring the integrity and quality of location and trajectory data.

Using GIS to visualize data quality

• Introduction to Data Analytics, including:

An introduction to current data science and data analytical tools and techniques

Analysis of their implications for data managers 

• Data Presentation:

Data Visualization

Data To Text

• Information Security Management:

Data confidentiality and rights management

Data integrity management, incident management and disaster recovery processes

Access control management

Data security strategy and policies

Data security classification

Risk assessment

Industry standards related to security

Auditing and assessing the business’ security management processes and compliance to security classification

• Entitlements and Obligations:

The following list gives an indication of the topics to be considered in the specifically legal component of the course. In each case, the full range of perspectives of the various parties involved will be considered: for example, individual companies (including operators service companies, commercial data vendors, large, medium and small), and state entities (including National Oil Companies and regulatory authorities).

Legislative, regulatory, contractual and operational criteria and requirements for acquisition, retention, use, sharing, sale and disposal of data

Data transfer agreements (including between companies / entities and cross-border)

Intellectual property considerations

Specific issues in the context of transfers of undertakings

Specific issues raised by derivative work

Data collection and reporting obligations under petroleum licences, production sharing agreements and service contracts

Current industry trends of relevance to data managers (including amongst others the General Data Protection Regulation and transparency initiatives such as the Extractive Industries Transparency Initiative, EU Transparency Directive, Dodd-Frank Act s1504(q))

Possible conflicts between contractual confidentiality clauses and transparency requirements

Industry standards and policies for the publication of data

Special regulatory requirements (for example, enhanced data gathering and enhanced disclosure) in the context of unconventional hydrocarbon development (shale, tight, oil sands)

• Commercial and economic considerations of petroleum data, including from the perspective of operators, service companies and commercial data vendors.

The connection between data quality, uncertainty, risk and project economics

The role of data in investment proposals

The connection between data quality, uncertainty, risk and project economics

Understanding the business case for acquiring data

Defining agreements for the acquisition of data, including the incorporation of data quality in commercial agreements

Agreeing win-win contracts

The Project course will put a range of the above topics in practice, but this may differ per student.