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Summary

This study introduces a deep-learning workflow to accelerate compositional reservoir simulations by replacing slow flash calculations. The AI model significantly enhances simulation speed and accuracy for CO2 injection and other complex fluid behaviors.

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Area of Science:

  • Petroleum Engineering
  • Computational Science
  • Artificial Intelligence

Background:

  • Compositional reservoir simulations offer high accuracy but are computationally expensive.
  • Black-oil models are faster but less reliable for phase-equilibrium-sensitive problems like CO2 injection.

Purpose of the Study:

  • To develop a deep-learning workflow to replace conventional iterative flash calculations in compositional reservoir simulations.
  • To improve the computational efficiency and maintain accuracy of reservoir simulations.

Main Methods:

  • A deep-learning model was developed to perform classification (phase stability) and regression (equilibrium ratios).
  • The trained model was integrated into a compositional simulator and tested on benchmark cases, including phase-envelope reconstruction and gas injection problems.

Main Results:

  • Phase-envelope reconstruction showed mean relative errors below 1% for liquid fraction and phase compositions.
  • Production rates in a gas-injection problem had mean relative errors below 0.2%.
  • Significant efficiency gains were observed, with flash calculations up to 517% faster and phase-stability testing up to 2464% faster.

Conclusions:

  • The deep-learning workflow effectively replaces iterative flash calculations, offering substantial speedups.
  • The method maintains thermodynamic consistency and high accuracy, making it suitable for complex reservoir simulation scenarios.
  • This approach enables more practical application of accurate compositional simulations for enhanced oil recovery and carbon sequestration.