Compositional Streamline-Based Modeling of Polymer Flooding Including Rheology, Retention, and Salinity Variation.

Area of Science:

• Petroleum Engineering
• Reservoir Simulation
• Enhanced Oil Recovery

Background:

• Polymer flooding is a widely used chemical enhanced oil recovery (CEOR) technology, facing challenges in adverse reservoir conditions.
• Current predictive tools for polymer flooding are often time-consuming, requiring detailed reservoir simulations.
• There is a need for efficient screening tools that capture key physical-chemical phenomena for effective project management.

Purpose of the Study:

• To develop a practical, two-dimensional (2D) polymer flooding model based on the streamlines approach.
• To create a tool that efficiently screens polymer flooding applications without sacrificing critical physical-chemical phenomena.
• To provide a faster alternative to traditional, time-consuming reservoir simulations for polymer flooding.

Main Methods:

• Developed a 2D streamline-based model incorporating shear-thinning/thickening rheology, salinity variations, permeability reduction, and polymer adsorption.
• Utilized a black oil formulation for pressure and saturation distribution, coupled with explicit composition computation.
• Validated the model against laboratory experiments, analytical results, field-scale simulations, and a CMG-STARS reference model.

Main Results:

• The developed streamline model effectively integrates key polymer flooding phenomena, offering a more complete description than existing simulators.
• The 1D tool along streamlines accurately represents polymer flooding's critical physical-chemical behaviors.
• The model demonstrates its capability to simplify upscaling from lab to field and identify favorable application scenarios.

Conclusions:

• The 2D streamline-based model provides a sound and quick tool for screening polymer flooding applications.
• This approach aids in understanding parameter impacts on oil recovery without extensive CEOR simulations.
• The developed tool facilitates better-fitted numerical models and supports cost-effective field application decisions.