The NeqSim Risk Framework provides comprehensive operational risk analysis capabilities for oil and gas operations. This documentation covers the advanced features implemented across seven priority areas.

Overview

The risk framework integrates with NeqSim’s process simulation capabilities to provide:

• Dynamic Risk Simulation (P1) - Monte Carlo with transient modeling
• SIS/SIF Integration (P2) - Safety Instrumented Systems per IEC 61508/61511
• Real-time Digital Twin (P3) - Continuous monitoring interface
• Bow-Tie Diagram Generation (P4) - Visual barrier analysis
• Multi-Asset Portfolio Risk (P5) - Portfolio-level VaR analysis
• Condition-Based Reliability (P6) - Predictive maintenance integration
• ML/AI Integration (P7) - Machine learning interface

Package Structure

neqsim.process.safety.risk
├── dynamic/           # P1: Dynamic simulation with transients
├── sis/               # P2: Safety Instrumented Systems
├── realtime/          # P3: Real-time monitoring
├── bowtie/            # P4: Bow-tie diagram analysis
├── portfolio/         # P5: Multi-asset portfolio risk
├── condition/         # P6: Condition-based reliability
├── ml/                # P7: ML/AI integration
└── examples/          # Quick-start examples

Quick Start

The following example is complete Java source. It builds a one-stream process, configures a reproducible dynamic risk screen, executes a one-day Monte Carlo simulation, and verifies the returned availability contract without console output. getMeanAvailability() returns a percentage from 0 to 100.

import neqsim.process.equipment.stream.Stream;
import neqsim.process.processmodel.ProcessSystem;
import neqsim.process.safety.risk.dynamic.DynamicRiskResult;
import neqsim.process.safety.risk.dynamic.DynamicRiskSimulator;
import neqsim.thermo.system.SystemInterface;
import neqsim.thermo.system.SystemSrkEos;

public final class RiskFrameworkReadmeExample {
  private RiskFrameworkReadmeExample() {}

  public static void main(String[] args) {
    SystemInterface fluid = new SystemSrkEos(298.15, 10.0);
    fluid.addComponent("methane", 1.0);
    fluid.setMixingRule("classic");

    Stream feed = new Stream("Feed", fluid);
    feed.setFlowRate(100.0, "kg/hr");

    ProcessSystem process = new ProcessSystem();
    process.add(feed);

    DynamicRiskSimulator simulator = new DynamicRiskSimulator(process);
    simulator.setFeedStreamName("Feed");
    simulator.setProductStreamName("Feed");
    simulator.setRandomSeed(2026L);
    simulator.addEquipmentMtbf("Compressor", 8760.0, 72.0);
    simulator.setTimestepHours(0.5);

    DynamicRiskResult result = simulator.runDynamicSimulation(10, 1.0);
    double meanAvailabilityPercent = result.getMeanAvailability();
    if (!Double.isFinite(meanAvailabilityPercent)
        || meanAvailabilityPercent < 0.0 || meanAvailabilityPercent > 100.0) {
      throw new IllegalStateException("Availability must remain in [0, 100] percent");
    }
  }
}

For comprehensive examples, see:

• RiskFrameworkQuickStart.java
• Advanced Risk Framework Tutorial

Feature Documentation

P1: Dynamic Simulation Integration

See Dynamic Simulation Guide

P2: SIS/SIF Integration

See SIS Integration Guide

P3: Real-time Digital Twin

See Real-time Monitoring Guide

P4: Bow-Tie Analysis

See Bow-Tie Analysis Guide

P5: Portfolio Risk

See Portfolio Risk Guide

P6: Condition-Based Reliability

See Condition-Based Reliability Guide

P7: ML/AI Integration

See ML Integration Guide

Industry Standards

The framework implements or aligns with:

Standard	Description	Package
IEC 61508	Functional Safety	sis
IEC 61511	Safety Instrumented Systems	sis
ISO 14224	Equipment Reliability Data	condition
ISO 31000	Risk Management	bowtie
NORSOK Z-013	Risk & Emergency Preparedness	All
OREDA	Offshore Reliability Data	dynamic

API Reference

• API Reference
• Mathematical Reference