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Quest Training

Managing Project Controls Using AI Tools for Refinery Projects Training Course

Equips refinery project teams to use AI for schedule analysis, cost forecasting, progress tracking, risk detection and validated cross-phase reporting.

DOW · Downstream Operations, Refining, Petrochemicals & Industrial OperationsAll Levels#3057ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Oil & GasCategories:Downstream Operations, Refining, Petrochemicals & Industrial Operations

Course Overview

This course explores the application of artificial intelligence (AI) tools to project controls in refinery projects, where complex engineering interfaces, long-lead equipment, construction sequencing, process safety requirements, and commissioning activities demand rigorous planning and performance monitoring. Participants learn how AI-assisted analysis can improve schedule visibility, cost forecasting, progress measurement, risk identification, and reporting across refinery engineering, procurement, construction, and commissioning phases. The programme focuses on refinery-specific applications, including process unit construction, piping and static equipment installation, rotating machinery, tankage, utilities, instrumentation and control systems, shutdown and turnaround activities, and pre-commissioning. Participants examine how AI, predictive analytics, and intelligent automation can support critical path analysis, identify productivity deviations, assess procurement delays, monitor quantities and work packages, forecast cost overruns, and evaluate the impact of engineering changes on project delivery. Through practical refinery project scenarios, participants develop workflows for integrating AI tools with project schedules, cost reports, progress measurement systems, risk registers, and engineering documentation. The course emphasizes data quality, validation of AI-generated insights, human oversight, cybersecurity, and coordination between project controls, engineering, procurement, construction, and commissioning teams.

Learning Objectives

  • Explain the role of AI in managing project controls throughout the refinery project lifecycle.
  • Identify suitable AI applications for engineering, procurement, construction, pre-commissioning, and commissioning activities.
  • Apply AI-assisted techniques to analyse refinery project schedules, critical paths, milestones, and construction sequencing.
  • Use predictive analytics to assess cost trends, estimate final project costs, and identify potential budget overruns.
  • Improve progress measurement through analysis of quantities installed, work packages, productivity, and earned value indicators.
  • Apply AI tools to identify procurement risks, long-lead equipment delays, and supply chain constraints.
  • Evaluate the schedule and cost implications of engineering changes, construction rework, and interface delays.
  • Integrate AI-assisted risk analysis into refinery project controls and corrective action planning.
  • Automate project reporting and validate AI-generated forecasts, explanations, and recommendations.
  • Develop an implementation roadmap for integrating AI tools into refinery project controls systems and workflows.

Who Should Attend

Project controls managers, engineers, and specialists working on refinery projects. Project managers and programme managers in oil and gas engineering and construction. Planning and scheduling engineers responsible for refinery project baselines and progress monitoring. Cost engineers, cost controllers, and project financial analysts. Engineering, procurement, and construction (EPC) project professionals. Construction managers and discipline engineers responsible for piping, mechanical, civil, electrical, instrumentation, and control systems. Procurement, expediting, contracts, and supply chain professionals managing refinery equipment and materials. Risk managers, project reporting specialists, and PMO professionals. Pre-commissioning, commissioning, and shutdown or turnaround planning professionals. Digital transformation and data analytics specialists supporting oil and gas project delivery.

Learning Outcomes

  • Explain how AI tools support planning, cost control, progress measurement, and risk management in refinery projects.
  • Analyse refinery project schedules to identify critical activities, interface constraints, and potential completion delays.
  • Evaluate engineering and construction progress using quantities, work packages, productivity data, and milestone achievement.
  • Develop AI-assisted cost forecasts and investigate variances between approved budgets, commitments, actual costs, and estimates at completion.
  • Identify potential delays involving long-lead equipment, materials, technical approvals, and vendor documentation.
  • Assess the effects of engineering revisions, rework, access restrictions, and construction sequencing changes on project performance.
  • Apply earned value management indicators to evaluate schedule and cost performance.
  • Use AI-assisted analysis to identify project risks, emerging trends, and potential corrective actions.
  • Generate consistent project controls reports while maintaining data integrity, traceability, and professional judgment.
  • Prepare an AI integration plan tailored to refinery project controls requirements, systems, and governance arrangements.

Course Outline

Day 1

AI Applications and Project Controls Fundamentals for Refinery Projects

  • Refinery project lifecycle and project controls requirements across feasibility, engineering, procurement, construction, pre-commissioning, and commissioning.
  • Integration of scope, schedule, cost, risk, resources, quantities, and progress reporting within refinery project controls.
  • Overview of AI technologies, including generative AI, machine learning, predictive analytics, and intelligent automation.
  • Refinery project data sources, including engineering deliverables, equipment registers, procurement schedules, construction reports, and cost systems.
  • Using AI tools to summarize technical documentation, extract project information, analyse reports, and support routine controls activities.
  • Data quality, coding structures, work breakdown structures (WBS), confidentiality, cybersecurity, and validation of AI-generated outputs.
  • Practical Exercise: Review a refinery project controls workflow and identify opportunities to apply AI across engineering, procurement, construction, and commissioning.
Day 2

AI-Assisted Planning, Scheduling, and Construction Progress Control

  • Developing and reviewing integrated refinery project schedules covering engineering deliverables, procurement packages, construction activities, and commissioning milestones.
  • Analysing critical paths, near-critical activities, schedule float, dependencies, and interfaces between refinery process units and supporting facilities.
  • Applying AI-assisted analysis to identify schedule slippage, sequencing conflicts, resource constraints, and potential completion delays.
  • Monitoring construction progress for piping installation, static equipment, rotating machinery, structural steel, tanks, electrical systems, and instrumentation.
  • Evaluating quantities installed, work packages completed, productivity rates, and progress measurement rules.
  • Developing early-warning indicators, progress dashboards, automated variance reports, and schedule recovery scenarios.
  • Practical Exercise: Analyse a simulated refinery construction schedule and progress dataset to identify critical delays, evaluate their impacts, and develop a schedule recovery action plan.
Day 3

AI for Cost Control, Procurement, and Earned Value Management

  • Establishing cost baselines and analysing budgets, commitments, actual expenditure, forecasts, and cost breakdown structures for refinery projects.
  • Using AI-assisted analytics to identify cost variances, unusual expenditure patterns, productivity losses, and potential overruns.
  • Applying earned value management concepts, including Planned Value (PV), Earned Value (EV), and Actual Cost (AC).
  • Interpreting Cost Performance Index (CPI) and Schedule Performance Index (SPI) to evaluate refinery project performance.
  • Forecasting Estimate at Completion (EAC) and identifying cost pressures arising from rework, delays, escalation, and scope changes.
  • Analysing procurement schedules, vendor documentation, manufacturing progress, logistics constraints, and long-lead equipment delivery risks.
  • Practical Exercise: Analyse a refinery project cost and procurement dataset, calculate key earned value indicators, identify forecast overruns, and prepare a cost and delivery risk report.
Day 4

AI-Driven Risk Management, Change Control, and Interface Coordination

  • Identifying refinery project risks across engineering, procurement, construction, process unit integration, and commissioning.
  • Using AI-assisted analysis to examine risk registers, technical queries, requests for information, non-conformance reports, and construction constraints.
  • Evaluating the effects of engineering changes, design revisions, piping modifications, equipment substitutions, and construction rework.
  • Assessing interface risks between process units, utilities, offsites, storage tanks, electrical systems, instrumentation, and control systems.
  • Applying scenario analysis to assess schedule and cost exposure arising from resource shortages, late materials, access constraints, and sequencing conflicts.
  • Using AI to support risk prioritization, mitigation planning, change impact assessments, and management escalation.
  • Practical Exercise: Evaluate a refinery project scenario involving delayed equipment delivery and engineering changes, quantify potential schedule and cost impacts, and prepare an integrated mitigation and change control plan.
Day 5

AI-Enabled Reporting, Commissioning Readiness, and Implementation

  • Designing AI-assisted dashboards and executive reports covering schedule performance, cost forecasts, construction progress, risks, and outstanding actions.
  • Automating progress summaries, variance explanations, procurement status reports, and project controls documentation.
  • Integrating construction completion information with systemization, mechanical completion, pre-commissioning, and commissioning schedules.
  • Monitoring punch lists, outstanding inspections, test packages, turnover documentation, and system handover milestones.
  • Establishing governance for AI-assisted project controls, including data access, audit trails, output validation, accountability, and human approval.
  • Developing implementation roadmaps, pilot use cases, performance measures, staff training, and continuous improvement plans for refinery projects.
  • Practical Exercise: Develop an AI-enabled refinery project controls dashboard and implementation roadmap incorporating schedule and cost indicators, construction progress, procurement risks, commissioning readiness, and data validation controls.

Upcoming Dates

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Barcelona · 7 – 11 Dec 2026 · Classroom

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