Refinery Reliability and Asset Performance Management Training Course

Refinery Reliability and Asset Performance Management Training Course

Introduction

Refinery Reliability and Asset Performance Management Training Course is designed to equip oil and gas professionals with the advanced technical knowledge, strategic capabilities, and practical tools required to maximize refinery equipment reliability, improve asset availability, and achieve operational excellence. This comprehensive training course explores the principles of refinery asset management, equipment criticality analysis, failure mode and effects analysis (FMEA), root cause analysis (RCA), reliability engineering, and condition-based monitoring. Participants will learn how to evaluate the performance of critical refinery assets, including pumps, compressors, turbines, heat exchangers, pressure vessels, pipelines, and rotating machinery, while aligning maintenance strategies with production targets, process safety requirements, and long-term business objectives.

Participants will explore key performance indicators (KPIs), overall equipment effectiveness (OEE), mean time between failures (MTBF), mean time to repair (MTTR), asset lifecycle cost analysis, and maintenance planning and scheduling. Through practical exercises, industry-based case studies, and reliability improvement scenarios, learners will develop actionable strategies for reducing equipment downtime, extending asset life, improving maintenance productivity, and increasing refinery profitability. By integrating international asset management principles, risk-based decision-making, and continuous improvement methodologies, this training course empowers professionals to establish sustainable reliability programs, strengthen maintenance governance, improve asset integrity, and build resilient refinery operations capable of delivering consistent performance, operational safety, and long-term value.

Course Duration

5 days

Course Objectives

By the end of this training course, participants will be able to:

1. Understand the principles of refinery reliability engineering and asset performance management to improve operational efficiency and equipment availability.
2. Develop comprehensive asset management strategies aligned with refinery production objectives and lifecycle performance requirements.
3. Apply reliability-centered maintenance (RCM) techniques to optimize maintenance activities and prevent recurring equipment failures.
4. Conduct failure mode and effects analysis (FMEA) to identify equipment vulnerabilities and prioritize corrective actions.
5. Implement root cause analysis (RCA) methodologies to eliminate chronic failures and improve long-term equipment reliability.
6. Utilize predictive maintenance and condition-based monitoring to detect early warning signs of equipment deterioration.
7. Evaluate asset performance using MTBF, MTTR, OEE, availability, and reliability KPIs to support data-driven decisions.
8. Apply risk-based maintenance and asset criticality analysis to prioritize resources for high-impact refinery equipment.
9. Integrate AI-powered predictive analytics and machine learning into refinery maintenance and reliability programs.
10. Improve maintenance planning, scheduling, and work management to reduce downtime and increase workforce productivity.
11. Optimize maintenance costs, lifecycle expenditure, and asset replacement decisions using cost-benefit and risk assessments.
12. Strengthen asset integrity management and process safety through effective inspection, monitoring, and maintenance controls.
13. Develop a sustainable continuous improvement and digital asset performance strategy that supports operational excellence and refinery profitability.

Target Audience

1. Refinery Maintenance Managers 
2. Reliability Engineers 
3. Asset Performance Managers 
4. Mechanical and Rotating Equipment Engineers
5. Maintenance Planners and Schedulers
6. Inspection and Asset Integrity Professionals 
7. Operations and Production Engineers a
8. Digital Transformation, Data Analytics, and Condition Monitoring Specialists 

Course Modules

Module 1: Fundamentals of Refinery Reliability and Asset Performance Management

• Principles of reliability engineering and modern refinery asset management.
• Relationship between reliability, availability, maintainability, and operational performance.
• Overview of asset lifecycle management from acquisition to decommissioning.
• Identification of critical refinery assets and their impact on production continuity.
• Case Study: Evaluating recurring failures in a refinery crude oil transfer pump system and developing a reliability improvement plan.

Module 2: Asset Criticality Analysis and Reliability-Centered Maintenance

• Principles and applications of reliability-centered maintenance (RCM).
• Asset criticality ranking based on safety, environmental, production, and financial consequences.
• Failure mode and effects analysis (FMEA) and failure mode, effects, and criticality analysis (FMECA).
• Selection of preventive, predictive, condition-based, and corrective maintenance strategies.
• Case Study: Prioritizing refinery compressors and pumps using criticality assessment and designing optimized maintenance tasks.

Module 3: Root Cause Analysis and Equipment Failure Prevention

• Root cause analysis (RCA) techniques for recurring mechanical and operational failures.
• Application of the five-whys method, fishbone diagrams, and fault tree analysis.
• Failure investigation involving vibration, lubrication, corrosion, overheating, and misalignment.
• Development of corrective and preventive action plans with measurable outcomes.
• Case Study: Investigating repeated seal failures in a hydrocarbon pump and implementing corrective measures to prevent recurrence.

Module 4: Predictive Maintenance and Condition-Based Monitoring

• Fundamentals of predictive maintenance and condition-based maintenance programs.
• Vibration analysis for rotating equipment fault detection and diagnostics.
• Infrared thermography, oil analysis, ultrasound, and equipment performance monitoring.
• Establishment of condition-monitoring thresholds, alarm limits, and intervention criteria.
• Case Study: Detecting early bearing deterioration in a refinery compressor using vibration trends and condition-monitoring data.

Module 5: Reliability KPIs, Performance Analytics, and Benchmarking

• Calculation and interpretation of mean time between failures (MTBF) and mean time to repair (MTTR).
• Measuring equipment availability, reliability, maintainability, and overall equipment effectiveness (OEE).
• Development of reliability dashboards, performance scorecards, and maintenance reports.
• Analysis of downtime losses, bad actors, failure trends, and maintenance effectiveness.
• Case Study: Using equipment downtime data to identify poor-performing refinery assets and develop a targeted performance improvement program.

Module 6: Digital Asset Performance Management, AI, and Industrial Analytics

• Introduction to asset performance management (APM) platforms and digital maintenance ecosystems.
• Applications of artificial intelligence (AI) and machine learning in failure prediction.
• Integration of industrial Internet of Things (IIoT) sensors, historians, and computerized maintenance management systems (CMMS).
• Use of real-time analytics, digital twins, and anomaly detection to support asset optimization.
• Case Study: Designing a predictive analytics framework for identifying potential failures in refinery rotating equipment using historical and real-time operating data.

Module 7: Maintenance Planning, Cost Optimization, and Lifecycle Management

• Development of preventive maintenance plans, job plans, and optimized maintenance schedules.
• Improving maintenance work order quality, backlog control, and spare parts availability.
• Lifecycle cost analysis, maintenance budgeting, and repair-versus-replacement decisions.
• Application of risk-based maintenance to prioritize expenditure and improve resource utilization.
• Case Study: Reducing maintenance costs and production interruptions by optimizing the maintenance strategy for a refinery heat exchanger network.

Module 8: Asset Integrity, Continuous Improvement, and Reliability Excellence

• Integration of reliability management with mechanical integrity and process safety programs.
• Inspection planning, corrosion management, and risk-based inspection principles.
• Application of reliability improvement methods, including PDCA, Lean maintenance, and Six Sigma.
• Development of asset performance improvement roadmaps, governance structures, and accountability systems.
• Case Study: Developing a refinery-wide reliability improvement roadmap following repeated equipment failures, unplanned shutdowns, and maintenance cost overruns.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.