Imran Ghafoor

PhD-qualified Oil & Gas Reliability and Asset Integrity Specialist with 14+ years of international experience supporting maintenance optimization, predictive maintenance, operational reliability, and risk-based asset management across midstream and downstream oil & gas sectors, with additional exposure to upstream-associated infrastructure and production support facilities. Proven track record delivering high-impact integrity, reliability, and operational improvement programs for major Middle East operators including Saudi Aramco, ADNOC, PETRONAS, OQ, QAFCO, QATALUM, and KOC. Experienced in advising clients on Risk-Based Inspection (RBI), Fitness-for-Service (FFS), Reliability Centered Maintenance (RCM), failure analysis, predictive maintenance, and asset lifecycle optimization for pipelines, refineries, gas plants, LNG facilities, and critical industrial infrastructure.

Strong background in maintenance and reliability engineering aimed at reducing operational risk, improving asset availability, optimizing inspection strategies, and supporting sustainable operational performance. Possesses a strong analytical and quantitative foundation with expertise in predictive analytics, anomaly detection, Remaining Useful Life (RUL) prediction, vibration analysis, AI-enabled condition monitoring, and data-driven asset performance methodologies. Skilled in translating complex technical findings into practical business recommendations that improve reliability, operational efficiency, and maintenance decision-making.

Demonstrated success leading multidisciplinary technical projects, managing client relationships, supporting proposal development, and collaborating with cross-functional teams across complex industrial and operational environments. Combines deep engineering expertise with strong communication, problem-solving, and stakeholder engagement capabilities to support operational excellence and long-term client value creation.

Research Engineer – Smart Infrastructure & Reliability - Chinese National Engineering Research Centre

(2025-08)

• Lead advanced Structural Health Monitoring (SHM) initiatives for critical infrastructure using Distributed Acoustic Sensing (DAS) and Fibre Bragg Grating (FBG) technologies.
• Develop AI-driven predictive maintenance algorithms for vibration and acoustic monitoring systems to support early-stage anomaly detection and asset reliability enhancement.
• Implement real-time data acquisition and integrity assessment systems for infrastructure monitoring applications.
• Support technical proposal development and collaborative research funding initiatives.
• Present technical findings at international conferences and industry workshops.

Research Engineer – AI-Driven Asset Reliability - Centre for Advances in Reliability and Safety, Hong Kong - Hong Kong

(2023-03 - 2025-06)

• Developed data-driven models using vibration sensor signals to detect and classify anomalies, enhancing asset reliability—techniques applicable to railways, pipeline and equipment monitoring in oil & gas.
• Conducted Accelerated Life Testing (ALT) and Physics of Failure analysis using Fishbone Diagrams and FMECA to identify root causes and critical failure modes in mechanical systems.
• Applied AI and statistical methods to predict aging behaviour and Remaining Useful Life (RUL) of assets, supporting predictive maintenance strategies for high-value infrastructure.
• Authored technical reports, integrity assessments, and research publications supporting industrial reliability applications.

Asset Integrity Engineer - BUDA Surveying Limited, Hong Kong - Hong Kong

(2022-07 - 2023-02)

• Executed advanced pipeline condition assessments using magnetic bracelet probe (BP) and ultrasonic testing (UT) to quantify wall thickness loss and detect defects, generating critical integrity data for asset health scoring.
• Pioneered the use of a smart Harmonized Flexible Printed Coils (HFPC) sensor-based system for automated ultrasonic pipeline inspection, enhancing data collection efficiency and accuracy for predictive maintenance models.
• Performed data-informed Remaining Life Assessments (RLA) by correlating inspection findings with operational parameters, providing actionable forecasts to optimize replacement schedules and capital planning.
• Supported project management and client interfacing by identifying and mitigating technical risks that threatened project timelines and budgets, ensuring on-time, on-budget delivery of integrity scopes.
• Led and participated in safety/risk workshops, applying systematic methodologies to prioritize asset threats and define risk-based inspection and maintenance strategies.

Doctoral Research Engineer – Advanced NDT & Asset Integrity - Smart Engineering Asset Management Laboratory

(2018-09 - 2022-06)

• Conducted doctoral research in advanced NDTs, focusing on ultrasonic guided wave techniques, FEM Simulations, Signal Processing, AI-based anomaly detection, and predictive modeling—directly applicable to asset integrity diagnostics in oil & gas.
• Designed and implemented experiments and field tests to evaluate the performance of smart Harmonized Flexible Printed Coils (HFPC) for contact-type ultrasonic NDT of wall-covered pipes.
• Specialized in Asset Integrity and Reliability Engineering through coursework in Asset and Maintenance Management, Risk and Decision Analysis, and Managerial Decision-Making Systems with Artificial Intelligence (AI).
• Contributed to technical proposals and applied research projects related to smart inspection technologies.

Asset Integrity Engineer (Project Leader) - Applus Velosi, Abu Dhabi, UAE - Abu Dhabi, UAE

(2012-07 - 2018-08)

• Led a team of engineers in delivering RBI and FFS programs for major energy clients (Saudi Aramco, Oman Oil, PETRONAS, QATALUM, QAFCO, KOC) across a portfolio of gas plants, refineries, and pipelines, leveraging data to drive inspection planning and resource allocation.
• Developed and implemented RBI plans per API 580/581 for critical facilities, including Sulfur Recovery Units, Crude Distillation Units, and Gas Gathering plants, to predict and prioritize asset threats, resulting in optimized inspection schedules and enhanced safety.
• Performed Fitness-for-Service (FFS) assessments per API 579-1/ASME FFS-1, encompassing Level 1 screening, Level 2 analytical evaluations, and coordination of Level 3 FEA-based assessments to define safe operating limits, justify life extension, and inform risk-based inspection plans.
• Performed integrity assessments for the Trans-Anatolian Natural Gas Pipeline (TANAP), developing a comprehensive baseline inspection plan for a 1,850km pipeline system to support its long-term integrity management strategy.
• Acted as primary client consultant for integrity strategy, presenting findings, justifying inspection plans, and supporting business development through proposal contributions.
• Performed Reliability Centered Maintenance (RCM) assessments for Pressure Safety Valves (PSVs), analyzing failure modes and effects to define optimal maintenance strategies (predictive, preventive, run-to-fail) that ensured functional reliability while minimizing lifecycle costs.
• Led Failure Modes and Effects Analysis (FMEA) for complex assets like Floating LNG mooring systems, systematically analyzing potential failures to inform maintenance and design improvements.
• Managed baseline survey projects involving advanced NDT techniques, establishing initial asset condition benchmarks essential for tracking degradation and enabling predictive maintenance models.

PhD - System Engineering and Engineering Management - City University of Hong Kong (2018 - 2022)