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Oil tanker safe operations risk analysis assignment

📅 February 17, 2026 ✍️ Cpapers ⏱ 4 min read

MARSO702: Assessment 2 – Risk-Based Operational Analysis Report (2026)

Module Overview

Module Code: MARSO702
Module Title: Maritime Safety and Operations
Level: 7 (MSc)
Credits: 20
Delivery: UK/Australia/US maritime academies (e.g., Warsash Maritime School)
Assessment Type: Individual Technical Report
Weighting: 50% of module mark
Word Count: 3,000–3,500 words (±10%, excluding references/appendices)
Submission: Via LMS (Turnitin-enabled PDF/Word) by [insert date]

Assessment Context

Oil tankers handle over 50% of global crude oil transport, where operational risks like spills, fires, and structural failures demand rigorous analysis under IMO conventions such as MARPOL, SOLAS, and ISM Code. Regulators and class societies require operators to demonstrate risk-based decision-making through structured assessments that integrate hazard identification, quantitative evaluation, and barrier management. This task mirrors real-world safety cases used in flag state audits and aligns with Formal Safety Assessment (FSA) guidelines, preparing you for roles in tanker operations, safety management, or consultancy.

Task Description

Conduct a risk-based operational analysis of safe oil tanker operations, focusing on key phases: cargo loading, transit, and discharge. Select a realistic scenario, such as a VLCC loading crude at a Nigerian terminal or STS transfer at sea.

Your report must:

  • Define operational boundaries, stakeholders (e.g., crew, terminal, charterer), and relevant regulations (e.g., ISGOTT, OCIMF guidelines).

  • Identify hazards using HAZID/HAZOP or bow-tie method.

  • Assess risks with a 5×5 matrix (likelihood x severity), prioritising top 5–7.

  • Propose layered barriers (preventive/recovery) and evaluate residual risk.

  • Recommend prioritised actions linked to SMS improvement.

Include at least two visual aids (e.g., risk matrix, bow-tie diagram) in appendices.

Report Structure

  1. Executive Summary (200 words): Scenario overview, key risks, top recommendations.

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  2. Introduction (400 words): Scenario description, aims, scope.

  3. Regulatory Framework (400 words): IMO/MARPOL/ISM alignment.

  4. Hazard Identification (600 words): Method, tabulated outputs.

  5. Risk Assessment (800 words): Matrix, assumptions, key drivers.

  6. Control Measures (600 words): Barriers, effectiveness analysis.

  7. Recommendations (400 words): Action plan, monitoring.

  8. Conclusion (200 words): Findings synthesis.

  9. References: 15+ Harvard-style sources (2018–2026).

  10. Appendices: Matrices, diagrams.

Requirements

  • Formatting: 1.5 spacing, Arial 11pt, Harvard referencing.

  • Visuals: 2–4 diagrams/tables; label/cite.

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  • Originality: <15% similarity; AI detection checked.

  • Sources: Peer-reviewed journals, IMO docs, OCIMF reports.

Learning Outcomes

  • Apply risk analysis to tanker operations.

  • Evaluate barriers under international standards.

  • Develop defensible safety arguments.

Marking Rubric

Criterion Weight Excellent (80–100%)  Good (60–79%) Satisfactory (40–59%) Fail (<40%)
Knowledge & Regulation 20% Nuanced integration of IMO/OCIMF with recent cases Solid coverage, minor gaps Basic, some inaccuracies Superficial/misapplied
Hazard/Risk Analysis 30% Rigorous HAZID/bow-tie, quantified uncertainties Clear method, some assumptions Descriptive, limited structure Unstructured/incomplete
Barriers & Recommendations 25% Layered, ALARP-justified, SMS-linked Practical, partially evaluated Generic lists Irrelevant/unsafe
Structure & Communication 15% Professional, visuals enhance argument Logical, minor flaws Readable but disjointed Disorganised/unclear
Referencing & Originality 10% 15+ recent sources, flawless Harvard Adequate, few errors Limited/inconsistent Absent/plagiarised

 (Executive Summary Example)

Cargo loading on a 300,000 DWT VLCC at a West African terminal poses risks from overpressurisation, spills, and human error during ballast exchange. Primary hazards include vapour cloud explosions (likelihood: occasional, severity: catastrophic, initial risk: high) and manifold leaks (medium risk). Barriers such as high-level alarms and double-block valves reduce residual risk to ALARP, but fatigue monitoring gaps persist. Prioritised actions involve VR simulator training and AI predictive analytics for pump failures. Implementation within SMS yields 40% risk reduction, aligning with IMO FSA goals (Wang et al., 2021, https://doi.org/10.1016/j.ress.2021.107956).[4]

References (Harvard)

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