AI Applications In Oil & Gas
AI for Predictive Maintenance of Rotating Equipment in Oil & Gas
This practical course helps professionals master predictive maintenance for pumps, compressors, turbines, motors, vibration data, and condition monitoring. The program connects key concepts, real use cases, risks, tools, and operational decisions so participants can apply the learning in their work environment. It can be tailored to the organization’s sector, internal systems, participant maturity, and performance objectives.
Objectives
- Understand the concepts, challenges, and use cases related to predictive maintenance for pumps, compressors, turbines, motors, vibration data, and condition monitoring.
- Identify the data, systems, processes, and stakeholders required for effective implementation.
- Assess risks, limitations, governance requirements, and practical control points.
- Use methods, tools, and templates to structure analysis and decision-making.
- Translate learning into action plans, recommendations, and measurable improvement opportunities.
- Adapt the approach to the operating context, team maturity, and business objectives.
Target audience
- Petroleum, production, drilling, and reservoir engineers
- Operations, maintenance, and reliability professionals
- Data, digital oilfield, and digital transformation teams
- Asset managers and performance leaders
- IT/OT specialists supporting oil and gas operations
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: Oil and Gas Use Case Framing and Value Definition
Foundation for Oil and Gas Use Case Framing and Value Definition: application, analysis, and review points linked to the module
Terminology and decisions in Oil and Gas Use Case Framing and Value Definition: application, analysis, and review points linked to the module
Inputs required for Oil and Gas Use Case Framing and Value Definition: application, analysis, and review points linked to the module
Typical mistakes around Oil and Gas Use Case Framing and Value Definition: applied exercise and practical decision from a realistic scenario
Module 2: Operational Data, Historian, SCADA, ERP, and Maintenance Sources
Current-state mapping for Operational Data, Historian, SCADA, ERP, and Maintenance Sources: application, analysis, and review points linked to the module
Examples and scenarios involving Operational Data, Historian, SCADA, ERP, and Maintenance Sources: application, analysis, and review points linked to the module
Diagnostic questions about Operational Data, Historian, SCADA, ERP, and Maintenance Sources: application, analysis, and review points linked to the module
Evidence produced through Operational Data, Historian, SCADA, ERP, and Maintenance Sources: applied exercise and practical decision from a realistic scenario
Module 3: Data Preparation, Feature Engineering, and Quality Controls
Design considerations for Data Preparation, Feature Engineering, and Quality Controls: application, analysis, and review points linked to the module
Roles and responsibilities in Data Preparation, Feature Engineering, and Quality Controls: application, analysis, and review points linked to the module
Exceptions and constraints affecting Data Preparation, Feature Engineering, and Quality Controls: application, analysis, and review points linked to the module
Quality checks for Data Preparation, Feature Engineering, and Quality Controls: applied exercise and practical decision from a realistic scenario
Module 4: Model Selection, Baselines, Validation, and Explainability
Operating model for Model Selection, Baselines, Validation, and Explainability: application, analysis, and review points linked to the module
Tools and workflow steps in Model Selection, Baselines, Validation, and Explainability: application, analysis, and review points linked to the module
Handoffs and approvals around Model Selection, Baselines, Validation, and Explainability: application, analysis, and review points linked to the module
Escalation points in Model Selection, Baselines, Validation, and Explainability: applied exercise and practical decision from a realistic scenario
Module 5: Workflow Integration with Engineers, Operators, and Dashboards
Performance measures for Workflow Integration with Engineers, Operators, and Dashboards: application, analysis, and review points linked to the module
Review routines after Workflow Integration with Engineers, Operators, and Dashboards: application, analysis, and review points linked to the module
Improvement actions for Workflow Integration with Engineers, Operators, and Dashboards: application, analysis, and review points linked to the module
Sustaining discipline around Workflow Integration with Engineers, Operators, and Dashboards: applied exercise and practical decision from a realistic scenario
Module 6: Risk, Cybersecurity, Governance, and Human-in-the-Loop Controls
Advanced scenarios in Risk, Cybersecurity, Governance, and Human-in-the-Loop Controls: application, analysis, and review points linked to the module
Failure patterns seen in Risk, Cybersecurity, Governance, and Human-in-the-Loop Controls: application, analysis, and review points linked to the module
Coordination challenges during Risk, Cybersecurity, Governance, and Human-in-the-Loop Controls: application, analysis, and review points linked to the module
Recovery actions for Risk, Cybersecurity, Governance, and Human-in-the-Loop Controls: applied exercise and practical decision from a realistic scenario
Module 7: Performance Measurement, ROI, Adoption, and Continuous Improvement
Governance requirements for Performance Measurement, ROI, Adoption, and Continuous Improvement: application, analysis, and review points linked to the module
Data quality checks in Performance Measurement, ROI, Adoption, and Continuous Improvement: application, analysis, and review points linked to the module
Risk controls related to Performance Measurement, ROI, Adoption, and Continuous Improvement: application, analysis, and review points linked to the module
Value measures for Performance Measurement, ROI, Adoption, and Continuous Improvement: applied exercise and practical decision from a realistic scenario
Module 8: Oil and Gas AI Implementation Workshop
Implementation planning for Oil and Gas AI Implementation Workshop: application, analysis, and review points linked to the module
Readiness questions before Oil and Gas AI Implementation Workshop: application, analysis, and review points linked to the module
Pilot design for Oil and Gas AI Implementation Workshop: application, analysis, and review points linked to the module
Lessons learned after Oil and Gas AI Implementation Workshop: applied exercise and practical decision from a realistic scenario
Materials provided
- ○ Slides used during the sessions
- ○ Group activities and practical exercises
- ○ Worksheets, checklists, and templates
- ○ Case studies relevant to the course
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for technical queries and guidance
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy designs technical and professional programs around the client’s operating reality. The course can be adapted to sector requirements, internal systems, team capability, practical use cases, and the level of depth required by the audience.
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