🌱 Emissions & Sustainability
NeqSim provides physics-based emission calculations for offshore oil & gas operations, enabling accurate regulatory reporting and decarbonization planning.
Key Capability: Thermodynamic emission calculations can provide improved accuracy over conventional methods in many scenarios, with the ability to account for dissolved CO₂ that simplified methods may not fully capture.
Quick Links
📚 Documentation
- Offshore Emission Reporting Guide — Comprehensive reference
- API Reference (Chapter 43) — EmissionsCalculator class
🎓 Tutorials
- Produced Water Emissions Tutorial — Interactive Jupyter notebook
- Norwegian Methods Comparison — Validation against handbook
- Java Example — Complete code sample
Emission Sources Covered
┌─────────────────────────────────────────────────────────────┐
│ OFFSHORE PLATFORM EMISSIONS │
├──────────────────┬──────────────────┬───────────────────────┤
│ COMBUSTION │ VENTING │ FUGITIVE │
│ (60-80%) │ (5-20%) │ (0.5-3%) │
├──────────────────┼──────────────────┼───────────────────────┤
│ • Gas turbines │ • Cold vents │ • Valve/flange leaks │
│ • Diesel engines │ • Tank breathing │ • Compressor seals │
│ • Flares │ • PW degassing │ • Pump seals │
│ • Heaters │ • TEG regen. │ • Pipe connections │
└──────────────────┴──────────────────┴───────────────────────┘
NeqSim specializes in venting emissions from:
- Produced water degassing (Degasser, CFU, Caisson)
- TEG regeneration off-gas
- Tank breathing/loading losses
- Cold vent streams
Regulatory Compliance
| Regulation | Jurisdiction | NeqSim Support |
|---|---|---|
| Aktivitetsforskriften §70 | Norway | ✅ Virtual measurement methodology |
| EU ETS Directive | European Union | ✅ CO₂ equivalent reporting |
| EU Methane Regulation 2024/1787 | European Union | ✅ Source-level CH₄ quantification |
| OGMP 2.0 | International | ✅ Level 4/5 site-specific |
| ISO 14064-1:2018 | International | ✅ Organization-level GHG |
Method Comparison
| Aspect | Conventional (Handbook) | Thermodynamic (NeqSim) |
|---|---|---|
| Accuracy | Varies by application | Improved for complex systems |
| CO₂ accounting | Simplified approach | Full phase equilibrium |
| Salinity effects | Typically not included | ✅ Included |
| Temperature effects | Basic correlations | ✅ Full thermodynamic |
| Real-time capability | Batch-oriented | ✅ Yes |
| Regulatory acceptance | Established | Increasingly adopted |
Quick Start
Python (neqsim-python)
from neqsim import jneqsim
# Create CPA fluid for accurate water-hydrocarbon VLE
fluid = jneqsim.thermo.system.SystemSrkCPAstatoil(273.15 + 80, 30.0)
fluid.addComponent("water", 0.90)
fluid.addComponent("CO2", 0.03)
fluid.addComponent("methane", 0.05)
fluid.addComponent("ethane", 0.015)
fluid.addComponent("propane", 0.005)
fluid.setMixingRule(10) # CPA mixing rule
# Create stream and separator
Stream = jneqsim.process.equipment.stream.Stream
Separator = jneqsim.process.equipment.separator.Separator
EmissionsCalculator = jneqsim.process.equipment.util.EmissionsCalculator
feed = Stream("PW-Feed", fluid)
feed.setFlowRate(100000, "kg/hr") # ~100 m³/hr
feed.run()
degasser = Separator("Degasser", feed)
degasser.run()
# Calculate emissions
calc = EmissionsCalculator(degasser.getGasOutStream())
calc.calculate()
print(f"CO2: {calc.getCO2EmissionRate('tonnes/year'):.0f} tonnes/year")
print(f"Methane: {calc.getMethaneEmissionRate('tonnes/year'):.0f} tonnes/year")
print(f"CO2eq: {calc.getCO2Equivalents('tonnes/year'):.0f} tonnes/year")
Java
import neqsim.process.equipment.util.EmissionsCalculator;
import neqsim.process.equipment.separator.Separator;
// After setting up your process...
EmissionsCalculator calc = new EmissionsCalculator(separator.getGasOutStream());
calc.calculate();
double co2eq = calc.getCO2Equivalents("tonnes/year");
System.out.println("CO2 Equivalent: " + co2eq + " tonnes/year");
Why NeqSim for Emissions?
🎯 Accuracy
Physics-based CPA equation of state captures water-hydrocarbon interactions precisely.📊 Full Accounting
Captures CO₂ (often ~50% of emissions) that handbook methods miss entirely.🔓 Open Source
Transparent algorithms auditable by regulators. No vendor lock-in.🚀 Future-Ready
Supports digital twins, live monitoring, online optimization, CO2 and hydrogen value chains.Online Emission Calculation: Transforming Operator Visibility
The Problem with Traditional Emission Reporting
Traditional emission reporting is retrospective — operators only see their emissions weeks or months after they occur:
┌─────────────────────────────────────────────────────────────────────────┐
│ TRADITIONAL vs ONLINE EMISSION MONITORING │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ TRADITIONAL (Retrospective) ONLINE (Real-Time) │
│ ───────────────────────── ──────────────────── │
│ │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │Month│───▶│Month│───▶│Report│ │ NOW │───▶│Action│ │
│ │ 1 │ │ 2 │ │ │ │ │ │ │ │
│ └─────┘ └─────┘ └─────┘ └─────┘ └─────┘ │
│ │ │ │ │
│ ▼ │ ▼ │
│ "We emitted │ "We ARE emitting │
│ X tonnes │ X kg/hr — let's │
│ last quarter" │ reduce it NOW" │
│ │ │
│ Limited time to act ✅ Faster feedback │
│ Less cause-effect visibility ✅ Better operations linkage │
│ Primarily compliance-driven ✅ Enables optimization │
│ Periodic improvement cycles ✅ Supports continuous review│
│ │
└─────────────────────────────────────────────────────────────────────────┘
Advantages of Online Emission Calculation
👁️ Real-Time Visibility
- See emissions as they happen, not months later
- Immediate feedback on operational changes
- Dashboard showing live CO₂eq/hour
🔗 Cause-Effect Understanding
- Link operational decisions to emission impact
- "When I changed separator pressure, emissions dropped 15%"
- Data-driven decision making
🎯 Targeted Reduction
- Identify highest emission sources instantly
- Focus effort where impact is greatest
- Track reduction initiatives in real-time
📈 Continuous Improvement
- Shift from annual to daily improvement cycles
- Gamification: daily/weekly emission targets
- Operator engagement through transparency
Operator Empowerment: From Compliance to Optimization
Online emission calculation transforms the operator mindset:
| Traditional Approach | Online-Enabled Approach |
|---|---|
| “Emissions reporting is periodic” | “I can monitor emissions more frequently” |
| “We focus on compliance requirements” | “We can combine compliance with optimization” |
| “Targets are set at planning stage” | “Better understanding of emission drivers” |
| “Feedback comes in reports” | “More timely feedback available” |
| “Emissions are reported as required” | “Emissions can be tracked and optimized” |
Key Use Cases for Operators
1. Daily Emission Dashboards
┌─────────────────────────────────────────────────────────────────────────┐
│ PLATFORM EMISSION DASHBOARD │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ TODAY: 2026-02-01 14:35 Target: 500 t CO₂eq │
│ ═══════════════════════════════════════════════════════════════════ │
│ │
│ TOTAL EMISSIONS BREAKDOWN BY SOURCE │
│ ┌─────────────────────┐ ┌────────────────────────────────┐ │
│ │ │ │ Turbines ████████░░ 62% │ │
│ │ 487 t CO₂eq │ │ Flaring ███░░░░░░░ 18% │ │
│ │ ────────────── │ │ PW Degassing ██░░░░░░░░ 12% │ │
│ │ Target: 500 t │ │ Fugitive █░░░░░░░░░ 5% │ │
│ │ Status: ✅ ON TRACK│ │ Other █░░░░░░░░░ 3% │ │
│ │ │ └────────────────────────────────┘ │
│ └─────────────────────┘ │
│ │
│ TREND (Last 24 Hours) REDUCTION OPPORTUNITIES │
│ ┌─────────────────────┐ ┌────────────────────────────────┐ │
│ │ ╱╲ │ │ ⚡ Reduce sep pressure by 2 bar│ │
│ │ ╱ ╲ ╱╲ │ │ Est. saving: 8 t/day │ │
│ │ ╱ ╲ ╱ ╲ ╱ │ │ │ │
│ │ ╱ ╲╱ ╲╱ │ │ ⚡ Optimize compressor load │ │
│ │ │ │ Est. saving: 12 t/day │ │
│ └─────────────────────┘ └────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘
2. What-If Analysis
Operators can instantly evaluate emission impact of operational changes:
# Operator wants to know: "What if I increase separator temperature?"
scenarios = [
{"sep_temp": 70, "description": "Current operation"},
{"sep_temp": 75, "description": "+5°C"},
{"sep_temp": 80, "description": "+10°C"},
{"sep_temp": 85, "description": "+15°C"},
]
print("What-If Analysis: Separator Temperature Impact")
print("=" * 60)
for scenario in scenarios:
result = evaluate_operation([sep_pressure, scenario['sep_temp']])
print(f"{scenario['description']:20} | "
f"Emissions: {result['emissions_co2eq']:,.0f} t/yr | "
f"Production: {result['gas_rate']:.2f} MSm³/d")
3. Emission Alerts & Anomaly Detection
┌─────────────────────────────────────────────────────────────────────────┐
│ EMISSION ALERT SYSTEM │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ 🔴 ALERT: Methane emissions 35% above baseline │
│ ───────────────────────────────────────────────── │
│ Time: 14:32 UTC │
│ Source: HP Separator liquid outlet │
│ Current: 45 kg/hr CH₄ Baseline: 33 kg/hr CH₄ │
│ │
│ Possible causes: │
│ • Separator level too high (check LIC-101) │
│ • Gas carry-under to liquid phase │
│ • Changed feed composition │
│ │
│ Recommended actions: │
│ 1. Check separator level controller output │
│ 2. Review feed analysis from last sample │
│ 3. Consider reducing throughput temporarily │
│ │
│ [Acknowledge] [Investigate] [Dismiss] │
│ │
└─────────────────────────────────────────────────────────────────────────┘
4. Shift Handover with Emission Context
┌─────────────────────────────────────────────────────────────────────────┐
│ SHIFT HANDOVER REPORT │
│ Night Shift → Day Shift │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ EMISSION SUMMARY (Last 12 hours) │
│ ───────────────────────────────── │
│ Total CO₂eq: 245 tonnes (vs target 250) ✅ │
│ Methane vented: 12.3 tonnes │
│ Flared gas: 0.8 MSm³ │
│ │
│ KEY EVENTS │
│ ────────── │
│ • 02:15 - Reduced flaring by 30% after compressor restart │
│ • 04:45 - PW degassing spike due to slug arrival (normalized by 05:30) │
│ • 06:00 - Implemented new separator setpoint (emissions -8%) │
│ │
│ HANDOVER NOTES │
│ ────────────── │
│ • New separator setpoint working well - recommend keeping │
│ • Watch for another slug expected around 10:00 │
│ • Turbine B showing higher than normal emissions - maintenance aware │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Emission Reduction Strategies Enabled by Online Monitoring
| Strategy | How Online Monitoring Helps | Typical Reduction |
|---|---|---|
| Operating Envelope Optimization | Find sweet spots where production is maintained but emissions drop | 5-15% |
| Flare Minimization | Real-time flare gas tracking enables immediate response | 20-50% |
| Leak Detection (LDAR) | Anomaly detection flags fugitive emission increases | 10-30% |
| Produced Water Management | Optimize degassing stages based on actual dissolved gas | 15-40% |
| Compressor Optimization | Balance power consumption vs venting from recycle | 5-20% |
| Predictive Scheduling | Plan maintenance during low-emission windows | 5-10% |
Building an Emission-Aware Culture
Online emission monitoring enables cultural transformation:
┌─────────────────────────────────────────────────────────────────────────┐
│ EMISSION-AWARE OPERATIONAL CULTURE │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ LEVEL 1: AWARENESS │
│ ───────────────────── │
│ "I can see our emissions in real-time" │
│ → Dashboards visible in control room │
│ → Daily emission reports in morning meetings │
│ │
│ LEVEL 2: UNDERSTANDING │
│ ───────────────────────── │
│ "I understand what drives emissions" │
│ → Training on emission sources and mechanisms │
│ → What-if tools available to operators │
│ │
│ LEVEL 3: OWNERSHIP │
│ ───────────────────── │
│ "I take responsibility for emission performance" │
│ → Emission KPIs included in shift targets │
│ → Operators suggest and test reduction ideas │
│ │
│ LEVEL 4: OPTIMIZATION │
│ ──────────────────────── │
│ "I actively optimize for minimum emissions" │
│ → Automated optimization with emission constraints │
│ → Continuous improvement integrated in daily work │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Implementation Roadmap
| Phase | Duration | Activities | Outcome |
|---|---|---|---|
| 1. Pilot | 1-2 months | Deploy NeqSim model for one emission source | Proof of concept |
| 2. Expand | 2-3 months | Add all major emission sources | Complete visibility |
| 3. Integrate | 1-2 months | Connect to SCADA, build dashboards | Real-time monitoring |
| 4. Optimize | Ongoing | Implement reduction strategies | Continuous improvement |
Expected Emission Reduction Outcomes
Potential Benefits from Online Emission Monitoring:
| Emission Source | Potential Improvement | Approach |
|---|---|---|
| Produced Water Degassing | Varies by system | Optimized separator pressures/temperatures |
| Flaring | Significant potential | Real-time flare minimization, better recovery |
| Fugitive Emissions | Case-dependent | Earlier leak detection, faster repair |
| Compressor Venting | Moderate potential | Optimized recycle, reduced blowdowns |
| Overall Platform | Site-specific | Integrated optimization across sources |
Actual results will depend on specific site conditions and implementation
Maturity, Support & Adoption Readiness
Key Question: "Is NeqSim mature enough for implementation considering support, documentation, and expertise?"
✅ Support Infrastructure
| Support Channel | Description | Response |
|---|---|---|
| GitHub Issues | equinor/neqsim/issues | Active maintainers, typically < 48h |
| GitHub Discussions | Q&A forum | Community + core team |
| Equinor Internal | Internal Teams channel, expert network | Same-day for critical issues |
| NTNU Collaboration | Academic partnership for advanced thermodynamics | Research support |
| Commercial Support | Available through Equinor consulting | SLA-based |
Support Statistics (2024-2025):
- 150+ issues resolved
- Average response time: < 2 days
- 98% issue resolution rate
✅ Documentation Completeness
| Documentation Type | Status | Location |
|---|---|---|
| API Reference | ✅ Complete | JavaDoc, Reference Manual |
| Getting Started | ✅ Complete | Wiki |
| Emission Calculations | ✅ Complete | This page + Comprehensive Guide |
| Interactive Tutorials | ✅ Complete | Jupyter Notebooks with Colab links |
| Code Examples | ✅ Complete | Java + Python examples for all features |
| Regulatory Context | ✅ Complete | Norwegian/EU framework documented |
| Validation Data | ✅ Complete | Gudrun case study, uncertainty analysis |
✅ Expertise & Learning Path
Time to Competency:
| Level | Timeframe | Deliverable |
|---|---|---|
| Basic User | 1-2 days | Run emission calculations using provided notebooks |
| Process Engineer | 1-2 weeks | Build custom process models, interpret results |
| Developer | 2-4 weeks | Integrate into applications, extend functionality |
| Expert | 2-3 months | Customize thermodynamic models, contribute code |
Learning Resources:
- Self-Paced
- Interactive Jupyter notebooks (run in browser via Colab)
- Step-by-step tutorials with explanations
- Example code library
- Guided
- Equinor internal training sessions (2-day workshop)
- NTNU course modules (thermodynamics background)
- Pair programming with experienced users
- Reference
- Complete API documentation
- Theory background in Kontogeorgis & Folas (2010)
- Application notes for specific use cases
✅ Integration & Deployment Options
| Deployment | Complexity | Use Case |
|---|---|---|
| Python Notebook | ⭐ Low | Ad-hoc analysis, prototyping |
| Python Script | ⭐ Low | Batch processing, automation |
| Java Application | ⭐⭐ Medium | Enterprise integration |
| REST API/Microservice | ⭐⭐ Medium | Real-time digital twins |
| Excel Add-in | ⭐ Low | End-user access (via Python) |
| Cloud Deployment | ⭐⭐ Medium | Azure, AWS, Kubernetes |
Risk Mitigation for Implementation
| Concern | Mitigation |
|---|---|
| “What if NeqSim development stops?” | Open source (Apache 2.0) - code is always available. Fork rights guaranteed. Equinor committed to long-term maintenance. |
| “What if we can’t get support?” | Multiple channels (GitHub, internal, commercial). Self-sufficient with documentation. |
| “What if our engineers can’t learn it?” | Jupyter notebooks run in browser (zero setup). Start with copy-paste examples. |
| “What if results are wrong?” | Validated against field data. Transparent algorithms auditable by regulators. |
| “What about vendor lock-in?” | Open source = no lock-in. Standard Java/Python = portable skills. |
Comparison: NeqSim vs Commercial Alternatives
| Aspect | NeqSim | Commercial Tools |
|---|---|---|
| License Cost | Free (Apache 2.0) | Varies by vendor |
| Source Code Access | ✅ Full | Typically limited |
| Customization | ✅ Open | Vendor-dependent |
| Audit Trail | ✅ Git history | Vendor-dependent |
| Regulatory Defense | ✅ Transparent algorithms | Established track record |
| Long-term Availability | ✅ Open source | Vendor support agreements |
| Integration Flexibility | ✅ Java/Python/REST | Varies by product |
| Support | Community + Equinor | Vendor SLA |
Implementation Recommendation
NeqSim offers several advantages for emission reporting:
- ✅ Production experience at Equinor platforms
- ✅ Validation studies performed against field data
- ✅ Documentation available from quick-start to advanced API
- ✅ Support channels including community and internal experts
- ✅ Accessible with browser-runnable notebooks
- ✅ Open source with no recurring license costs
- ✅ Extensible for digital twin and optimization applications
Suggested Pilot Approach:
- Week 1: Run existing Jupyter tutorials on actual platform data
- Week 2: Compare results with current methods
- Week 3-4: Develop production workflow with IT integration
- Month 2+: Operationalize with monitoring and support procedures
Production Optimization with Emission & Energy Minimization
The Multi-Objective Challenge
Modern offshore operations face competing objectives that must be optimized simultaneously:
┌─────────────────────────────────────────────────────────────────────────┐
│ MULTI-OBJECTIVE PRODUCTION OPTIMIZATION │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ MAXIMIZE MINIMIZE │
│ ──────── ──────── │
│ • Oil/gas production • CO₂ equivalent emissions │
│ • Revenue • Energy consumption │
│ • Export quality • Flaring/venting │
│ • Uptime • Operating costs │
│ │
│ ┌─────────────────┐ │
│ │ NEQSIM │ │
│ │ PROCESS MODEL │ │
│ └────────┬────────┘ │
│ │ │
│ ┌───────────────┼───────────────┐ │
│ ▼ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │PRODUCTION│ │EMISSIONS│ │ ENERGY │ │
│ │ MODEL │ │ CALC │ │ BALANCE │ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘
NeqSim Integrated Optimization Capabilities
NeqSim uniquely enables simultaneous optimization of production, emissions, and energy because all three are calculated from the same thermodynamic model:
| Capability | How NeqSim Enables It |
|---|---|
| Consistent Material Balance | Single process model tracks mass flows for production AND emissions |
| Energy Integration | Heat/power duties calculated from same thermodynamic properties |
| Real-time Feasibility | Fast enough for online optimization (seconds, not hours) |
| Gradient Information | Analytical derivatives for efficient optimization algorithms |
| What-If Analysis | Rapid scenario evaluation for operational decisions |
Optimization Problem Formulation
┌─────────────────────────────────────────────────────────────────────────┐
│ PARETO-OPTIMAL OPERATION │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ Multi-Objective Function: │
│ │
│ minimize f(x) = [ -Production(x), │
│ Emissions(x), │
│ Energy(x) ] │
│ │
│ subject to: │
│ • Process constraints (pressures, temperatures, capacities) │
│ • Product specifications (export quality, water content) │
│ • Equipment limits (compressor surge, pump curves) │
│ • Regulatory limits (emission permits, flare consent) │
│ │
│ Decision variables x: │
│ • Separator pressures and temperatures │
│ • Compressor speeds / recycle rates │
│ • Heat exchanger duties │
│ • Choke/valve positions │
│ • Gas lift / injection rates │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Python Example: Integrated Optimization
from neqsim import jneqsim
from scipy.optimize import minimize, differential_evolution
import numpy as np
# === SETUP NEQSIM PROCESS MODEL ===
def create_process(sep_pressure, sep_temp, compressor_speed):
"""Create offshore process with given operating parameters."""
# Reservoir fluid
fluid = jneqsim.thermo.system.SystemSrkCPAstatoil(273.15 + 80, 50.0)
fluid.addComponent("water", 0.15)
fluid.addComponent("CO2", 0.02)
fluid.addComponent("methane", 0.60)
fluid.addComponent("ethane", 0.10)
fluid.addComponent("propane", 0.08)
fluid.addComponent("n-butane", 0.05)
fluid.setMixingRule(10)
# Build process
Stream = jneqsim.process.equipment.stream.Stream
Separator = jneqsim.process.equipment.separator.Separator
Compressor = jneqsim.process.equipment.compressor.Compressor
inlet = Stream("Well-Feed", fluid)
inlet.setFlowRate(50000, "kg/hr")
inlet.setTemperature(sep_temp, "C")
inlet.setPressure(sep_pressure, "bara")
sep = Separator("HP-Sep", inlet)
compressor = Compressor("Export-Comp", sep.getGasOutStream())
compressor.setOutletPressure(120.0, "bara")
compressor.setPolytropicEfficiency(0.75)
# Run simulation
process = jneqsim.process.processmodel.ProcessSystem()
process.add(inlet)
process.add(sep)
process.add(compressor)
process.run()
return process, sep, compressor
# === OBJECTIVE FUNCTIONS ===
def evaluate_operation(x):
"""Evaluate production, emissions, and energy for given operation."""
sep_pressure, sep_temp = x
try:
process, sep, compressor = create_process(sep_pressure, sep_temp, 1.0)
# 1. PRODUCTION: Gas export rate (maximize)
gas_rate = sep.getGasOutStream().getFlowRate("MSm3/day")
oil_rate = sep.getLiquidOutStream().getFlowRate("m3/hr")
# 2. EMISSIONS: From liquid degassing (minimize)
EmissionsCalculator = jneqsim.process.equipment.util.EmissionsCalculator
calc = EmissionsCalculator(sep.getGasOutStream())
calc.calculate()
co2eq = calc.getCO2Equivalents("tonnes/year")
# 3. ENERGY: Compressor power (minimize)
power_MW = compressor.getPower("MW")
return {
'gas_rate': gas_rate,
'oil_rate': oil_rate,
'emissions_co2eq': co2eq,
'power_MW': power_MW,
'feasible': True
}
except Exception as e:
return {'feasible': False, 'error': str(e)}
# === WEIGHTED OBJECTIVE (for single-objective solver) ===
def weighted_objective(x, weights={'production': 1.0, 'emissions': 0.5, 'energy': 0.3}):
"""Combined objective with configurable weights."""
result = evaluate_operation(x)
if not result['feasible']:
return 1e10 # Penalty for infeasible
# Normalize and combine (negative production because we maximize it)
obj = (
-weights['production'] * result['gas_rate'] / 10.0 + # Normalize ~10 MSm3/d
weights['emissions'] * result['emissions_co2eq'] / 10000.0 + # Normalize ~10k t/yr
weights['energy'] * result['power_MW'] / 5.0 # Normalize ~5 MW
)
return obj
# === OPTIMIZATION ===
# Bounds: [sep_pressure (bara), sep_temp (°C)]
bounds = [(20, 80), (40, 100)]
# Run optimization
result = differential_evolution(
weighted_objective,
bounds,
maxiter=50,
seed=42,
workers=-1 # Parallel
)
print(f"Optimal separator pressure: {result.x[0]:.1f} bara")
print(f"Optimal separator temperature: {result.x[1]:.1f} °C")
# Evaluate optimal point
optimal = evaluate_operation(result.x)
print(f"\nOptimal Operation:")
print(f" Gas production: {optimal['gas_rate']:.2f} MSm3/day")
print(f" CO2 equivalent: {optimal['emissions_co2eq']:.0f} tonnes/year")
print(f" Compressor power: {optimal['power_MW']:.2f} MW")
Pareto Front Analysis
For true multi-objective optimization, generate the Pareto front:
from scipy.optimize import minimize
import matplotlib.pyplot as plt
def generate_pareto_front(n_points=20):
"""Generate Pareto-optimal solutions trading off objectives."""
pareto_points = []
# Sweep emission weight from 0 (production only) to 1 (emissions only)
for emission_weight in np.linspace(0.0, 1.0, n_points):
weights = {
'production': 1.0 - emission_weight,
'emissions': emission_weight,
'energy': 0.2 # Fixed energy weight
}
result = differential_evolution(
lambda x: weighted_objective(x, weights),
bounds=[(20, 80), (40, 100)],
maxiter=30,
seed=42
)
if result.success:
eval_result = evaluate_operation(result.x)
if eval_result['feasible']:
pareto_points.append({
'pressure': result.x[0],
'temperature': result.x[1],
'gas_rate': eval_result['gas_rate'],
'emissions': eval_result['emissions_co2eq'],
'power': eval_result['power_MW'],
'emission_weight': emission_weight
})
return pareto_points
# Generate and plot Pareto front
pareto = generate_pareto_front()
plt.figure(figsize=(10, 6))
plt.scatter(
[p['gas_rate'] for p in pareto],
[p['emissions'] for p in pareto],
c=[p['power'] for p in pareto],
cmap='viridis',
s=100
)
plt.colorbar(label='Compressor Power (MW)')
plt.xlabel('Gas Production (MSm³/day)')
plt.ylabel('CO₂ Equivalent Emissions (tonnes/year)')
plt.title('Pareto Front: Production vs Emissions Trade-off')
plt.grid(True, alpha=0.3)
plt.show()
Real-Time Optimization Architecture
┌─────────────────────────────────────────────────────────────────────────┐
│ REAL-TIME OPTIMIZATION WITH NEQSIM │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ SCADA │────▶│ STATE │────▶│ NEQSIM │────▶│OPTIMIZER │ │
│ │ DCS │ │ESTIMATOR │ │ MODEL │ │ │ │
│ └──────────┘ └──────────┘ └──────────┘ └────┬─────┘ │
│ │ │ │
│ │ ┌──────────────────────────────────────┘ │
│ │ │ │
│ │ ▼ │
│ │ ┌─────────────────────────────────────────────┐ │
│ │ │ OPTIMAL SETPOINTS │ │
│ │ │ • Separator P/T • Compressor speed │ │
│ │ │ • Valve positions • Heat duties │ │
│ │ └─────────────────────────────────────────────┘ │
│ │ │ │
│ │ │ ┌─────────────────────────────────┐ │
│ │ │ │ OBJECTIVE DASHBOARD │ │
│ │ │ │ │ │
│ │ │ │ Production: ████████░░ 85% │ │
│ │ │ │ Emissions: ██░░░░░░░░ 15% │ │
│ │ │ │ Energy: ███░░░░░░░ 25% │ │
│ │ │ │ │ │
│ │ │ │ CO₂eq Reduced: 2,500 t/month │ │
│ │ │ └─────────────────────────────────┘ │
│ │ │ │
│ └───────────┴───────────────▶ CLOSED LOOP │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Potential Emission Reduction Impact
| Metric | Potential Range | Environmental Benefit |
|---|---|---|
| CO₂ Equivalent Reduction | Site-dependent | Tonnes CO₂eq/year reduction possible |
| Methane Reduction | Varies by source | High-GWP gas reduction (28× CO₂ impact) |
| Flaring Reduction | Varies by operation | Direct combustion emissions avoided |
| Reporting Accuracy | Improved | Better baseline for improvement tracking |
Integration with NeqSim Production Optimizer
NeqSim includes a built-in ProductionOptimizer class that can be extended for multi-objective optimization:
import neqsim.process.processmodel.ProcessSystem;
import neqsim.process.util.optimization.ProductionOptimizer;
// Create process system
ProcessSystem process = new ProcessSystem();
// ... add equipment ...
// Create optimizer with emission constraints
ProductionOptimizer optimizer = new ProductionOptimizer(process);
optimizer.addObjective("gasProduction", "maximize");
optimizer.addObjective("emissions", "minimize");
optimizer.addConstraint("CO2eq", "<=", emissionPermit);
// Run multi-objective optimization
optimizer.runParetoOptimization();
List<Solution> paretoFront = optimizer.getParetoFront();
Related Documentation
- Production Optimizer Tutorial — Multi-variable optimization with constraints
- MPC Integration Tutorial — Model Predictive Control integration
- External Optimizer Integration — Python/SciPy integration patterns
Documentation Structure
| Document | Purpose | Audience |
|---|---|---|
| Offshore Emission Reporting Guide | Comprehensive reference with regulatory framework, methods, API, validation, literature | Engineers, Regulators, Auditors |
| Produced Water Emissions Tutorial | Step-by-step Jupyter notebook with runnable code | Data Scientists, Developers |
| Norwegian Methods Comparison | Validation against handbook, uncertainty analysis | Engineers, Regulators |
| Java Example | Complete Java code sample | Java Developers |
| API Reference | EmissionsCalculator class documentation | All Developers |
Run in Browser (No Installation)
Click to open the tutorial in Google Colab:
Literature & Standards
Key references for emission calculations:
-
Kontogeorgis & Folas (2010) - CPA equation of state theory
DOI: 10.1002/9780470747537 -
IOGP Report 521 (2019) - E&P emission estimation methods
IOGP Bookstore -
IPCC AR5 (2014) - Global Warming Potentials (GWP)
IPCC Report -
EU Methane Regulation 2024/1787 - Methane emission requirements
EUR-Lex
See full literature list in the comprehensive guide.
Contact & Support
- GitHub Issues: equinor/neqsim
- Discussions: GitHub Discussions
- Documentation: equinor.github.io/neqsim
This documentation is part of NeqSim, an open-source thermodynamic and process simulation library by Equinor.