Introduction
The NeqSim Risk Simulation Framework provides comprehensive tools for analyzing equipment failures, their impact on production, and optimizing plant operation under degraded conditions. It integrates with NeqSim’s process simulation capabilities to provide physics-based risk assessment.
Key Concepts
1. Equipment Failure Modes
Equipment can fail in different ways, each with different consequences:
| Failure Type | Description | Capacity Factor |
|---|---|---|
| TRIP | Equipment stops completely | 0% |
| DEGRADED | Reduced capacity operation | 10-90% |
| PARTIAL_FAILURE | Some functions lost | 20-80% |
| FULL_FAILURE | Complete breakdown | 0% |
| MAINTENANCE | Planned shutdown | 0% |
| BYPASSED | Flow routed around | 0% (for that unit) |
2. Reliability Metrics
Standard reliability metrics from OREDA (Offshore Reliability Data):
| Metric | Symbol | Description |
|---|---|---|
| Mean Time To Failure | MTTF | Average operating time before failure |
| Mean Time To Repair | MTTR | Average repair duration |
| Mean Time Between Failures | MTBF | MTTF + MTTR |
| Failure Rate | λ | Failures per time unit |
| Availability | A | Fraction of time operational |
3. Risk Assessment
Risk is evaluated on two dimensions:
- Probability (likelihood of failure)
- Consequence (impact when failure occurs)
The combination determines the Risk Level:
Risk Level = f(Probability, Consequence)
4. Process Topology
Equipment connections form a directed graph:
Feed → Separator → Compressor → Cooler → Export
↘ Pump → Storage
Understanding topology enables:
- Cascade failure analysis
- Parallel equipment identification
- Critical path determination
Framework Capabilities
✅ Equipment Failure Modeling
- Define failure types (trip, degraded, partial, etc.)
- Capacity reduction factors
- Auto-recovery behavior
- Multi-source reliability data (IOGP/SINTEF, CCPS, IEEE 493, Lees, OREDA)
✅ Production Impact Analysis
- Quantify production loss from failures
- Compare failure scenarios
- Rank equipment by criticality
- Calculate revenue impact
✅ Monte Carlo Simulation
- Stochastic failure simulation
- P10/P50/P90 production estimates
- Availability calculations
- Confidence intervals
✅ Risk Matrix
- 5×5 probability-consequence matrix
- Color-coded risk levels
- Economic cost calculations
- Visual ASCII/JSON export
✅ Topology Analysis
- Graph extraction from ProcessSystem
- Topological ordering
- Parallel equipment detection
- DOT format export for visualization
✅ STID Tagging
- ISO 14224 compliant tag parsing
- Norwegian offshore installation codes
- Equipment type classification
- Parallel train identification
✅ Dependency Analysis
- “If X fails, what’s affected?”
- Cascade failure propagation
- Cross-installation dependencies
- Equipment monitoring recommendations
Portfolio risk analysis
The advanced framework’s P5 capability uses PortfolioRiskAnalyzer to aggregate asset availability losses and shared common-cause scenarios. Use it for production-loss screening, not as a safety acceptance or financial valuation model.
The input basis is maximum asset production in boe/day, an availability fraction from zero to one, scenario frequency in events/year, duration in days, and a fractional production impact for each affected asset. The simulation period is in years, with a 365-day year. Common-cause event counts are sampled from a Poisson distribution; their losses are added to the baseline availability losses.
PortfolioRiskResult reports production rates in boe/day and accumulated losses in boe over the simulation period. Its P10/P50/P90/P99 values are ascending loss percentiles: a higher percentile means a larger loss, not a higher production forecast. The value-at-risk accessor is a production-loss quantity, not monetary VaR.
The additive model does not reconcile overlapping outages or cap their combined loss at the available production. Reported portfolio availability is based on individual asset availability and does not deduct common-cause losses. Shared scenarios are not a general asset-correlation matrix. Validate the input basis, avoid double counting, and obtain independent engineering review before using these screening results for decisions. The portfolio regression tests show the implemented API and software checks; they are not independent field qualification.
Typical Workflow
1. Build Process Model
└─► ProcessSystem with equipment
2. Define Failure Scenarios
└─► EquipmentFailureMode for each critical unit
3. Assign Reliability Data
└─► MTTF, MTTR from OREDA
4. Build Topology
└─► ProcessTopologyAnalyzer.buildTopology()
5. Tag Equipment
└─► STID functional locations
6. Analyze Dependencies
└─► DependencyAnalyzer.analyzeFailure()
7. Build Risk Matrix
└─► RiskMatrix.buildRiskMatrix()
8. Run Monte Carlo
└─► OperationalRiskSimulator.runSimulation()
9. Optimize Degraded Operation
└─► DegradedOperationOptimizer.optimize()
Industry Standards
This framework follows industry standards:
| Standard | Application |
|---|---|
| ISO 14224 | Equipment taxonomy and reliability data |
| OREDA | Offshore reliability data handbook |
| ISO 31000 | Risk management principles |
| NORSOK Z-013 | Risk and emergency preparedness |
| IEC 61508 | Functional safety |
| API 580/581 | Risk-based inspection |
Mathematical Foundation
See Mathematical Reference for detailed formulas covering:
- Failure probability distributions
- Availability calculations
- Monte Carlo algorithms
- Risk cost functions
- Production loss modeling
Next Steps
- Equipment Failure Modeling - Define failure modes
- Risk Matrix - Build visual risk assessment
- Monte Carlo Simulation - Run stochastic analysis
- Topology Analysis - Extract process structure