Optimization and Uncertainty Quantification Method for Reservoir Stimulation through Carbonate Acidizing

Area of Science:

• Petroleum Engineering
• Geochemistry
• Computational Science

Background:

• Reservoir stimulation, particularly matrix acidizing, is crucial for enhancing oil and gas production from carbonate formations.
• Understanding parameter uncertainty is vital for optimizing stimulation effectiveness and predicting reservoir performance.

Purpose of the Study:

• To develop an uncertainty quantification and optimization workflow for matrix acidizing in carbonate reservoirs.
• To identify key parameters influencing stimulation efficiency and understand their impact on reservoir productivity.

Main Methods:

• A finite-element reactive transport model was developed and verified for simulating dissolution channel propagation.
• Surrogate modeling using polynomial chaos expansion (PCE) and Sobol indices were employed for sensitivity analysis.
• Dimensionless groups (Damköhler, Peclet, acid capacity numbers) were used to assess parameter effects.

Main Results:

• The surrogate model accurately reproduced physics-based simulation results, including dissolution channels and effective permeability.
• Global sensitivity analysis revealed the acid capacity number as the most significant parameter for pore volume to breakthrough.
• Uncertainty propagation was systematically assessed, providing physical insights into the matrix acidizing process.

Conclusions:

• The developed workflow offers a systematic approach for uncertainty analysis and optimization in rock stimulation processes.
• Accurate characterization of uncertainty enhances the understanding and predictability of matrix acidizing in hydrocarbon reservoirs.