Optimized Polymer Injection through Modelling: from Lab to Field

Engineers invest a significant number of hours to create a field development plan. Once implemented in the field, decisions based on poor data may damage the reservoir or result in lost revenue. Avoid these common pitfalls and learn how simple modelling tools help determine the appropriate type and quantity of chemicals based on reservoir characteristics. Using optimization leads to efficient use of time, and reduced risk while improving recovery, and protection of your valuable assets.

Optimize a polymer flood, using specific features from IMEX, GEM and STARS, to determine the best technical and economical recovery process. Common issues will be reviewed, including:

• Visco-elastic effects
• Relative permeability
• Adsorption
• Residual resistance factor
• Injectivity loss

The multi-step workflow begins by defining the polymer characteristics as well as its interactions with the reservoir, then observing the reservoir’s flow behavior during polymer injection, and finally optimizing the process using CMOST.

A calibrated fluid model is developed using CMOST AI’s automated history matching feature by matching a core flood simulation model to experimental lab data. The lab scale model is upscaled to field scale for predicting the polymer flood in an inverted 5-spot pattern. This history matching and upscaling procedure is performed for three different polymers. Finally, CMOST AI optimization identifies the polymer type and injection strategy that maximizes the oil recovery factor and NPV by considering the properties and costs of each polymer option.

How?