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LNG Terminal Valuation Analyst - OpenClaw Skill

Professional-grade LNG export terminal valuation using 100% public data sources

Data Sources License Python

🎯 What This Skill Does

This OpenClaw skill provides institutional-quality LNG terminal valuations using publicly available data. It's like having a Wood Mackenzie analyst in your pocket, but powered by free, open data sources.

Key Capabilities

  • 💰 DCF Valuations - Full discounted cash flow models with NPV, IRR, payback
  • 📊 Competitive Benchmarking - Compare terminals across owners, regions, technologies
  • 📈 Market Analysis - Track Henry Hub prices, utilization rates, forward curves
  • 🎯 Scenario Modeling - Bull/bear cases, stress tests, sensitivity analysis
  • 🔍 Terminal Search - Find assets by owner, status, capacity, location
  • 📉 Breakeven Analysis - Calculate critical utilization and tolling fees

🚀 Quick Start

Installation

# Clone or copy the skill to your OpenClaw skills directory
cp -r lng-valuation-skill ~/clawd/skills/

# Install dependencies
cd ~/clawd/skills/lng-valuation-skill
pip install pandas numpy requests --break-system-packages

# Test the installation
python lng_analyst.py

First Query

from lng_analyst import LNGAnalyst

analyst = LNGAnalyst()
response = analyst.handle_query("What's Sabine Pass worth?")
print(response)

📚 Usage Examples

1. Terminal Valuation

Query:

"Value Sabine Pass LNG"

Response:

═══════════════════════════════════════════════════════════
SABINE PASS LNG - VALUATION SUMMARY
═══════════════════════════════════════════════════════════

ASSET PROFILE
─────────────────────────────────────────────────────────
Owner:              Cheniere Energy
Location:           Cameron Parish, Louisiana
Capacity:           4.0 Bcf/d (30.0 mtpa)
Status:             Operating
Contract Coverage:  100%

VALUATION RESULTS (Base Case)
─────────────────────────────────────────────────────────
Enterprise Value:   $18.20B
NPV @ 8%:           $14.70B
IRR:                12.3%
Payback Period:     N/A (Operating Asset)

EV/EBITDA Multiple: 9.2x
Stabilized EBITDA:  $1,981M/year

KEY ASSUMPTIONS
─────────────────────────────────────────────────────────
Utilization:        95%
Tolling Fee:        $3.00/MMBtu
Discount Rate:      8.0%
Fixed Opex:         $0.40/MMBtu
Project Life:       25 years

BREAKEVEN ANALYSIS
─────────────────────────────────────────────────────────
Breakeven Utilization:  78.3%
Breakeven Tolling Fee:  $2.47/MMBtu

2. Competitive Comparison

Query:

"Compare all Cheniere terminals"

Response:

════════════════════════════════════════════════════════════════════════════
CHENIERE LNG TERMINALS - COMPETITIVE ANALYSIS
════════════════════════════════════════════════════════════════════════════

Terminal                  Owner            Capacity  Status      NPV    IRR   EV
                                          (Bcf/d)               ($B)   (%)  ($B)
────────────────────────────────────────────────────────────────────────────
Sabine Pass LNG          Cheniere Energy    4.0    Operating   14.7  12.3  18.2
Corpus Christi LNG       Cheniere Energy    2.0    Operating    7.4  12.1   9.1
Cheniere Corpus Stage 3  Cheniere Energy    2.7    Announced   11.2  10.8  15.6

════════════════════════════════════════════════════════════════════════════
TOTAL CAPACITY: 8.7 Bcf/d
AGGREGATE VALUE: $42.9B
════════════════════════════════════════════════════════════════════════════

3. Scenario Analysis

Query:

"Run scenario analysis on Cameron LNG"

Response:

════════════════════════════════════════════════════════════════════════════
CAMERON LNG - SCENARIO ANALYSIS
════════════════════════════════════════════════════════════════════════════

Scenario    NPV ($B)   IRR (%)   EV ($B)   EBITDA ($M)   Utilization   Fee
────────────────────────────────────────────────────────────────────────────
Bear Case      5.2      9.8       8.7         728            0.85      2.50
Base Case      7.4     12.1       9.1         991            0.95      3.00
Bull Case      9.8     14.6      10.2        1,254           1.05      3.50

════════════════════════════════════════════════════════════════════════════

KEY SENSITIVITIES:
────────────────────────────────────────────────────────

UTILIZATION_RATE:
  Range: 0.75 to 1.10
  NPV Impact: $3,421M

TOLLING_FEE:
  Range: 2.00 to 4.00
  NPV Impact: $2,847M

DISCOUNT_RATE:
  Range: 0.06 to 0.12
  NPV Impact: $1,956M

4. Market Analysis

Query:

"What's the current Henry Hub price?"

Response:

════════════════════════════════════════════════════════════════════════════
NATURAL GAS MARKET PRICES
════════════════════════════════════════════════════════════════════════════

Henry Hub Spot Price: $3.24/MMBtu
Date: 2026-02-17

12-MONTH FORWARD CURVE:
────────────────────────────────────────────────────────
Feb 2026: $3.24/MMBtu
May 2026: $3.26/MMBtu
Aug 2026: $3.28/MMBtu
Nov 2026: $3.30/MMBtu

════════════════════════════════════════════════════════════════════════════

5. Terminal Search

Query:

"Find all terminals under construction"

Response:

════════════════════════════════════════════════════════════════════════════
FOUND 5 TERMINALS
════════════════════════════════════════════════════════════════════════════

Terminal Name              Owner                     Capacity  Status           Start
                                                    (Bcf/d)
────────────────────────────────────────────────────────────────────────────────────
Plaquemines LNG Phase 2    Venture Global LNG         1.7    Under Construction  2026-01
Golden Pass LNG            QatarEnergy / ExxonMobil   2.4    Under Construction  2025-06
Rio Grande LNG Phase 1     NextDecade Corporation     3.6    Under Construction  2027-01
Port Arthur LNG            Sempra Infrastructure      1.7    Under Construction  2027-06

════════════════════════════════════════════════════════════════════════════
Total Capacity: 9.4 Bcf/d
════════════════════════════════════════════════════════════════════════════

🎓 Methodology

DCF Model Structure

Revenue Model:

  • Tolling fee-based (typical: $2.25-$3.50/MMBtu)
  • Capacity reservations
  • Utilization assumptions (EIA averages: 95-105%)

Cost Structure:

  • Fixed opex: $0.30-0.50/MMBtu capacity
  • Variable opex: Minimal (tolling model)
  • Capex: GGIT estimates + FERC filings

Financial Assumptions:

  • Discount rate: 7-10% (default 8%)
  • Tax rate: 21% (US federal)
  • Project life: 20-30 years (default 25)
  • Terminal value: Perpetuity growth method

Metrics:

  • Net Present Value (NPV)
  • Internal Rate of Return (IRR)
  • Payback period
  • EV/EBITDA multiple
  • Breakeven analysis

Data Sources

All data comes from authoritative public sources:

Data Category Source Update Frequency
Terminal assets Global Energy Monitor GGIT Quarterly
Capacity & status EIA, IEA Monthly
Gas prices FRED, EIA Daily
Utilization EIA Today in Energy Monthly
Industry benchmarks FERC, Company filings Ongoing

Data Quality vs. Proprietary Sources

Category Public Data Wood Mackenzie Gap
Terminal specs ✅ Excellent ✅ Excellent None
Market prices ✅ Excellent ✅ Excellent None
Utilization ✅ Good ✅ Real-time Timeliness
Cost models ⚠️ Moderate ✅ Proprietary Granularity
Contracts ⚠️ Limited ✅ Comprehensive Significant
Forecasts ⚠️ Basic ✅ Detailed Significant

Bottom Line: Public data provides 80-85% of the value for most analyses. Main gaps are in contract databases and proprietary forecasting models.

🛠️ Technical Architecture

Project Structure

lng-valuation-skill/
├── SKILL.md                 # OpenClaw skill definition
├── README.md                # This file
├── data_collector.py        # Public data acquisition
├── valuation_engine.py      # DCF models and analysis
├── lng_analyst.py           # Main interface
├── requirements.txt         # Python dependencies
└── tests/                   # Unit tests

Core Components

1. Data Collector (data_collector.py)

  • Fetches terminal database from Global Energy Monitor
  • Retrieves Henry Hub prices from FRED
  • Accesses EIA utilization data
  • Provides industry benchmarks

2. Valuation Engine (valuation_engine.py)

  • DCF model implementation
  • Sensitivity analysis
  • Scenario modeling
  • Breakeven calculations
  • Multi-terminal comparisons

3. LNG Analyst (lng_analyst.py)

  • Natural language query processing
  • Intent detection and routing
  • Response formatting
  • Conversational interface

Dependencies

pandas>=1.5.0
numpy>=1.23.0
requests>=2.28.0
scipy>=1.9.0  (for IRR calculations)

🎯 Use Cases

Investment Analysis

  • M&A Due Diligence - Value target terminals for acquisition
  • Portfolio Management - Stress-test terminal investments
  • Credit Analysis - Model debt service coverage ratios
  • Benchmarking - Compare terminal economics vs peers

Project Development

  • Feasibility Studies - Model greenfield terminal economics
  • Contract Structuring - Optimize tolling fees and capacity reservations
  • Technology Selection - Compare traditional vs. modular technologies
  • Market Entry - Identify whitespace and competitive positioning

Research & Analysis

  • Market Trends - Track capacity additions and utilization
  • Price Discovery - Understand feedgas costs and tolling economics
  • Competitive Intelligence - Monitor competitor projects and strategies
  • Regulatory Analysis - Model impact of export permits and emissions regulations

🚀 Advanced Features

Custom Scenarios

scenarios = {
    'Recession': {
        'utilization_rate': 0.70,
        'tolling_fee': 2.00,
        'discount_rate': 0.12
    },
    'Energy Crisis': {
        'utilization_rate': 1.10,
        'tolling_fee': 4.50,
        'discount_rate': 0.06
    }
}

results = valuation.scenario_analysis(scenarios)

Sensitivity Analysis

# Test multiple variables
sensitivity = valuation.sensitivity_analysis(
    variables=['utilization_rate', 'tolling_fee', 'capex', 'discount_rate'],
    ranges={
        'utilization_rate': (0.70, 1.10),
        'tolling_fee': (2.00, 4.00),
        'capex': (0.8 * capex, 1.2 * capex),
        'discount_rate': (0.06, 0.12)
    }
)

Portfolio Analysis

# Value multiple terminals
terminals = collector.search_terminals(owner='Cheniere')
portfolio = compare_terminals(
    terminal_list=terminals,
    assumptions=ValuationAssumptions(discount_rate=0.08)
)

print(f"Total portfolio value: ${portfolio['EV ($B)'].sum():.1f}B")

📊 Example Output Files

The skill can export results in multiple formats:

  • Valuation Reports - Detailed DCF models with assumptions
  • Comparison Tables - Multi-terminal benchmarking
  • Scenario Matrices - Bull/base/bear case outputs
  • Sensitivity Charts - Tornado diagrams and waterfall charts
  • CSV Exports - Raw data for further analysis

🔬 Data Accuracy & Limitations

What This Skill Does Well

✅ Terminal-level asset screening and comparison ✅ DCF valuations with industry-standard methodologies ✅ Scenario and sensitivity analysis ✅ Market price tracking (Henry Hub, forward curves) ✅ Capacity and utilization analysis

Known Limitations

⚠️ Contract data - Only public filings available (vs. comprehensive databases) ⚠️ Real-time ops - Monthly EIA data (vs. daily monitoring) ⚠️ Proprietary forecasts - Basic price assumptions (vs. multi-scenario modeling) ⚠️ Global coverage - Best for US terminals (international data more limited)

When to Use Proprietary Data

Consider upgrading to Wood Mackenzie/IHS Markit when you need:

  • Granular contract terms and counterparty exposure
  • Real-time operational data and outage tracking
  • Detailed cost curves and construction economics
  • Proprietary price forecasts with probabilistic scenarios
  • Global LNG trade flow modeling

🤝 Contributing

This is an open-source skill. Contributions welcome!

Improvement Ideas

  • Add more international terminals (Australia, Qatar, Africa)
  • Integrate with more data sources (IHS, Platts)
  • Build machine learning price forecasting
  • Add visualization (matplotlib charts, plotly dashboards)
  • Create web interface (Streamlit/Gradio)
  • Expand to regasification terminals (import side)

Development Setup

# Clone repo
git clone https://github.com/yourusername/lng-valuation-skill

# Install dev dependencies
pip install -r requirements-dev.txt

# Run tests
python -m pytest tests/

# Format code
black lng_analyst.py data_collector.py valuation_engine.py

📄 License

MIT License - See LICENSE file for details

🙏 Acknowledgments

Data Sources:

Inspiration:

  • Wood Mackenzie Lens Gas & LNG
  • IHS Markit Vantage
  • Rystad Energy UCube

📞 Support

Questions? Issues? Ideas?

🎉 Get Started

# Install the skill
cp -r lng-valuation-skill ~/clawd/skills/
cd ~/clawd/skills/lng-valuation-skill
pip install -r requirements.txt --break-system-packages

# Run interactive mode
python lng_analyst.py

# Or use in your own code
from lng_analyst import LNGAnalyst
analyst = LNGAnalyst()
print(analyst.handle_query("Value Sabine Pass LNG"))

Welcome to professional LNG analysis powered by open data! 🚀

About

LNG terminal DCF valuation + shadow fleet intelligence. Live prices, sanctions tracking, no API key required.

Topics

python finance energy valuation dcf lng sanctions dark-fleet

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