Summary Cheng et al. (2000) present a simple method to fit foam-simulation parameters without oil to data for pressure gradient as a function of superficial velocities of gas and liquid. The key in this process is the identification of “high-quality” (high gas fraction) and “low-quality” foam regimes. The method is essentially the same for the foam-model parameters in foam models in STARS (Cheng et al. 2000), UTCHEM (Cheng et al. 2000), or ECLIPSE (Schlumberger 2010). Often, however, available data are more limited: pressure gradient for one scan of foam quality at fixed total superficial velocity. We show how to extend this method to the more-limited data set. The transition in regimes occurs at the foam quality with the maximum-pressure gradient. We illustrate the method by fitting parameters to several published data sets. Our approach is simple and direct. The model fit would be appropriate for an enhanced-oil-recovery process involving foam injection at finite water fraction, but not a surface-alternating-gas foam process involving large slugs of gas and liquid. For the latter process, the model fit should focus on data relevant to that process (i.e., at extremely high foam quality). The approach assumes an abrupt transition between high- and low-quality-foam regimes (e.g., a large value of epdry in the STARS foam model). If the transition is less abrupt, the parameter values quickly obtained by this method could provide the initial guess for a computer-based least-squares fit of all parameters, including a smaller value of epdry, and a check on the parameters so obtained.
Summary Foam improves sweep in miscible and immiscible gas-injection enhanced-oil-recovery processes. Surfactant-alternating-gas (SAG) foam processes offer many advantages over coinjection 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). This means that fitting data to a typical scan of foam behavior as a function of foam quality can miss conditions essential to the success of an SAG process. The result can be inaccurate scaleup of results to field application. We illustrate how to fit foam-model parameters to steady-state foam data for application to injection of a gas slug in an SAG foam process. Dynamic SAG corefloods can be unreliable for several reasons. These include 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 three parameters: 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 the fitting of these model parameters to coreflood data, and the challenges that can arise in the fitting process, with the published foam data of Persoff et al. (1991) and Ma et al. (2013). For illustration, we use the foam model in the widely used STARS (Cheng et al. 2000) simulator. Accurate water-saturation data are 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, inaccurate predicted injectivity: for instance, a model fit in which foam does not collapse even at 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.
Cheng et al. (2000) present a simple method to fit foam simulation parameters without oil to data for pressure gradient as a function of superficial velocities of gas and liquid. The key in this process is the identification of "high-quality" (high gas fraction) and "low-quality" foam regimes. The method is essentially the same for the foam model parameters in foam models in STARS, UTCHEM or ECLIPSE. Often, however, available data are more limited -pressure gradient for one scan of foam quality at fixed total superficial velocity. We show how to extend this method to the more limited data set. The transition in regimes occurs at the foam quality with the maximum pressure gradient. We illustrate the method by fitting parameters to several published data sets.Our approach is simple and direct. The model fit would be appropriate for an EOR process involving foam injection at finite water fraction, but not a SAG foam process involving large slugs of gas and liquid. For the latter process, model parameters should be fit to data relevant to that process, i.e. at extremely high foam quality.The approach assumes an abrupt transition between high-and low-quality foam regimes, i.e. a large value of epdry in the STARS foam model. If a smaller value is chosen for faster execution of the simulator this approach could underestimate pressure gradient near its maximum value at the transition between regimes. In that case the parameter values quickly obtained by this method could provide the initial guess for a computer-based least-squares fit of all parameters, including a smaller value of epdry, and a check on the parameters so obtained.
Gravity override is a severe problem in gas-injection enhanced-oil-recovery processes, especially in relatively homogeneous formations. Foam can reduce gravity override. Shan and Rossen (2004) show that the best foam process for overcoming gravity override is one of injecting a large slug of surfactant followed by a large slug of gas, injected at constant, maximum-allowable injection pressure. This process works because foam collapses near the injection well, giving good injectivity simultaneously with mobility control at the leading edge of the gas bank. The supply of gas that would be needed to maintain constant injection pressure is a concern for EOR processes where gas is produced industrially or from a separations plant with limited capacity: the available gas stream may not be sufficient for the optimal process. We show that for such a process, the pressure drop across the foam bank back to the injection well, at fixed injection rate, is nearly constant as the foam bank propagates radially outward. From this result, one can derive a simple formula to predict the rate of gas injection required for each of two limiting cases: an extremely strong foam at the foam front, many times more viscous than the fluids it displaces. In this case, the rate of gas injection required to maintain constant injection pressure is nearly constant, but injection rate is low. a foam just strong enough to maintain mobility control at its leading edge. In this case injection rate required to maintain constant injection pressure increases steeply with time. Using the formulae provides a quick initial estimate of how gas injection rate must vary over the duration of the EOR process to maintain an optimal process. In addition, we illustrate how one would determine the properties of a foam that would fit the available gas stream. This criterion then could guide the development of a surfactant formulation with these properties.
A B S T R A C TFoam is used in gas-injection EOR processes to reduce the mobility of gas, resulting in greater volumetric sweep. SAG (Surfactant Alternating Gas) is a preferred method of injection as it results in greater injectivity in the field, but designing a successful process requires knowledge of foaming performance at very high foam qualities (gas fractional flows).Here the use of foam in low-permeability (∼1 mD) Indiana Limestone cores for SAG foam applications is studied. Coreflood experiments were performed for a range of foam qualities at high pressure (100 bar), elevated temperature (55°C), high salinity (200,000 ppm) and in the presence of crude oil. The effectiveness of the foam was studied by differential pressure measurements along the core. Foam was still able to form under these stringent conditions, but it was a relatively weak foam (i.e. its ability to reduce gas mobility is modest). For one surfactant formulation, further analysis of the experimental results show that the foam would be able to maintain mobility control over the displaced phase, thus providing a stable displacement front, and that it can be used in a SAG foam process in these formations. For a second formulation the non-monotonic nature of the fractional-flow data requires further investigation before scale-up to the field.In addition, further coreflood experiments were carried out using heterogeneous, vuggy Edwards White cores with even lower permeability (∼0.5 mD). These experiments were performed to determine whether foaming is possible in heterogeneous media and especially to investigate the effects of disconnected vugs on the foaming performance. CT scans were taken during the period of foam injection to determine saturation profiles within the core. Foam was able to form inside these cores, but inside the vugs segregation was observed with liquid pockets visible in the bottom of the vugs and gas in the remainder. This segregation was only a local effect though, confined to the vug itself, and foam was able to persist in the rest of the core.
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