Foam improves sweep in miscible and immiscible gas-injection EOR processes. "SAG" foam processes (injecting alternating slugs of surfactant solution and gas) offer many advantages over co-injection of foam for both operational and sweep-efficiency reasons.  The success of a foam SAG process depends on foam behavior at very low injected water fraction (high foam quality), which means that fitting data to a typical scan of foam behavior as a function of foam quality can miss conditions that determine the success of a SAG process.  The result can be inaccurate scale-up of results to field application. A successful SAG foam process depends on behavior at very low fractional flow during gas injection and on behavior at larger water fractional flow further from the well where gas and surfactant slugs mix.

We illustrate how to fit foam-model parameters to steady-state foam data for application to SAG foam processes.  Dynamic SAG corefloods can be unreliable because of failure to reach local steady state  (because of slow foam generation), the increased effect of dispersion at the core scale, and the capillary end effect.  For current foam models the behavior of foam in SAG depends on the mobility of full-strength foam, the capillary pressure or water saturation at which foam collapses and the parameter governing the abruptness of this collapse.  We illustrate how to fit these model parameters to coreflood data, and the challenges that can arise in the fitting process, using the published foam data of Persoff et al. (1991) and Ma et al. (2012). For illustration we use the foam model in the widely used STARS simulator. Having accurate water-saturation data is essential to making a reliable fit to the data.  Model fits to a given experiment may result in inaccurate extrapolation to mobility at the wellbore and therefore predicted injectivity: for instance, a model fit in which foam does not collapse even at the extremely large capillary pressure at the wellbore.

We show how the insights of fractional-flow theory can guide the model-fitting process and give quick estimates of foam propagation rate, mobility and injectivity at the field scale.

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