CO₂-EOR Optimization Framework

PhD candidate: Petrenko Taras Sergiyovych
Advisor: Branimir Cvetcovich

This research develops novel computational methods for CO₂-EOR optimization, contributing:

1. Hybrid MMP correlation framework with improved accuracy (RMSE < 150 psi)
2. Physics-informed genetic algorithm with 20-30% faster convergence
3. GPU-accelerated sweep efficiency modeling
4. Field-validated uncertainty quantification

Key Publications:

• Petrenko, T. (2025). Study of physicochemical and geochemical aspects of enhanced oil recovery and CO₂ storage in oil reservoirs. Technology Audit and Production Reserves, 2(1(82)), 24–29. https://doi.org/10.15587/2706-5448.2025.325343

Research Methodology

1. Data Collection

   • TBD

2. Model Development

   • Hybrid MMP correlation development
   • GPU-accelerated optimization
   • Uncertainty quantification framework

3. Validation

   • Numerical simulation (ECLIPSE)
   • Field case studies
   • Sensitivity analysis

Key Features

• Comprehensive MMP Calculation

  • Multiple empirical correlations (Cronquist, Glaso, Yuan)
  • Temperature and composition dependent

• Advanced Data Processing

  • LAS file parsing with automatic unit conversion
  • ECLIPSE simulator data integration
  • Robust data validation

• Physics-Informed Optimization

  • Hybrid genetic algorithm + Bayesian optimization
  • GPU-accelerated calculations
  • Koval sweep efficiency modeling

• Visualization System

  • MMP depth profiles
  • Optimization convergence tracking
  • Parameter sensitivity analysis

Installation

From PyPI (recommended)

pip install co2eor-optimizer

From Source

# Clone repository
git clone https://github.com/fgfalll/WAG_optimisation.git
cd WAG_optimisation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/MacOS
venv\Scripts\activate     # Windows

# Install with development dependencies
pip install -e ".[dev]"

Quick Start

1. Import your reservoir data (LAS or ECLIPSE format)
2. Calculate MMP for your reservoir conditions
3. Run optimization to determine optimal injection parameters

Usage Example

from co2eor_optimizer import MMPCalculator, OptimizationEngine

# Calculate MMP using Yuan correlation
mmp = MMPCalculator().calculate_mmp(
    temperature=180,  # °F
    api_gravity=32,
    gas_composition={'CO2': 0.95, 'N2': 0.05}
)

# Optimize injection scheme
results = OptimizationEngine().optimize_recovery(
    reservoir_data='eclipse_data.DATA',
    constraints={'max_injection_pressure': 5000}  # psi
)

print(f"Optimal WAG ratio: {results.optimal_wag_ratio}")

Documentation

Comprehensive documentation is available in the Doc directory:

• Architecture Overview - System design and components
• Development Timeline - Project milestones
• Code Audit Review - Quality assurance report
• Code Audit Review from 16.06 - Quality assurance report
• API Reference - Detailed module documentation

Contributing

We welcome contributions! Please see our:

• Contribution Guidelines
• Code of Conduct

Key areas for contribution:

• Additional MMP correlations
• Enhanced visualization features
• Simulator integration improvements

Roadmap

• Add support for CMG simulator data
• Hybrid GH MMP correlation (completed in v1.2)
• Advanced PVT integration (viscosity modeling, EOS support)
• Enhanced GPU acceleration (multi-GPU support, memory optimization)
• Implement machine learning-based MMP prediction
• Develop UI (PyQT6)
• Field data integration module (ECLIPSE results visualization)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For technical inquiries: engineering@saynos2011@gmail.com

Researcher:
@fgfalll