Specialised Technical Workshop
Design and Development of Digital Twins and AI Applications in the Oil & Gas Industry
Develop digital twin and AI application design skills by connecting operational use cases, data interfaces, modelling assumptions and validation requirements.
Course Overview
Examines development of digital twins and AI applications for oil and gas workflows. Participants review data connections, modelling assumptions and validation needed to support operational decisions.
Who This Course Is For
- Reservoir and development engineers
- Petrophysicists and subsurface analysts
- Field evaluation technical teams
- Data specialists supporting reservoir studies
What Participants Will Learn
Define use cases and decision objectives
Map data sources and integration requirements
Review model design and application assumptions
Define validation checks and monitoring arrangements
Outline a validated operational application design
Key topics
- Data integration
- Model design
- AI application scope
- Validation and monitoring
Course Modules
Back to Course OverviewUse Cases and Decision Objectives
Use Cases and Decision Objectives
Module overview
Define the intended operational decision and identify the scope, users and evidence needed for a digital twin or AI application.
Key topics
Data integration · Model design · AI application scope · Validation and monitoring
Learning outcome
Participants can define use cases and decision objectives.
Learning format
Lectures, design workshop and application case studies
Data Sources and Integration
Data Sources and Integration
Module overview
Map data connections and review quality, availability and interface requirements that affect application design.
Key topics
Source mapping · Data availability · Interface requirements
Learning outcome
Participants can map data sources and integration requirements.
Learning format
Technical review · diagram exercises
Model Design and Assumptions
Model Design and Assumptions
Module overview
Connect model structure with the intended use case, documenting assumptions and questions about how outputs will be interpreted.
Key topics
Model structure · Assumption records · Output interpretation
Learning outcome
Participants can review model design and application assumptions.
Learning format
Worked examples · guided analysis
AI Components and Application Scope
AI Components and Application Scope
Module overview
Assess the role of AI within the proposed workflow and define the evidence needed to evaluate its outputs.
Key topics
Workflow roles · Output evaluation · Evidence requirements
Learning outcome
Participants can assess AI components and evaluation requirements.
Learning format
Case discussion · planning exercise
Validation and Operational Monitoring
Validation and Operational Monitoring
Module overview
Plan validation and monitoring arrangements that support review of application outputs as data and operating conditions change.
Key topics
Validation criteria · Monitoring arrangements · Change review
Learning outcome
Participants can define validation checks and monitoring arrangements.
Learning format
Practical case · group review
Application Design Workshop Case
Application Design Workshop Case
Module overview
Develop a use case outline, review data and model dependencies, and explain the validation requirements supporting its intended decisions.
Key topics
Use case outline · Dependency review · Validation plan
Learning outcome
Participants can outline a digital twin use case and define validation requirements.
Learning format
Integrated case · group review
Programme Information
Course duration, location, practical components, training-centre information and applicable certification arrangements depend on the selected programme.
Learning format: Lectures, design workshop and application case studies
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