Simulation of Surfactant-Polymer Floods with a Novel Microemulsion Equation of State

Abstract

Reservoir simulation is a valuable tool for assessing the potential success of enhanced recovery processes. Current chemical flooding reservoir simulators, however, use Hand's model to describe surfactant-oilbrine systems even though Hand's model is not predictive, and can fit only a limited data set. Hand's model requires the tuning of multiple empirical parameters using experimental data that usually consist of salinity scans at constant reservoir temperature and atmospheric pressure. Given experimental data supporting the change in microemulsion phase behavior with key formulation properties (e.g. temperature, pressure, salinity, EACN, and overall composition), there is a need for an improved model that can capture changes in these relevant parameters at the reservoir scale. The recent EOS proposed for microemulsion phase behavior Johns 2014, 2016), which is based partially on the hydrophyllic-lypophyllic difference and net average curvature model (HLD-NAC, Acosta et al. 2003), has been supported by numerous experimental data and provides a more mechanistic phase behavior model than the Hand's model.In this paper, the EOS model with the extension to two-phase regions is incorporated for the first time into the chemical flooding simulators, UTCHEM, and our new in-house simulator PennSim. Hand's model is only used for comparison purposes, and is no longer needed even for flash calculations in the type II-and type IIϩ regions. The results show excellent agreement between UTCHEM and PennSim both in composition space and for composition/saturation profiles. Further, the HLD-NAC based EOS model and Hand's models are fitted to the same experimental data and the results of these simulations are nearly identical when variations of salinity, pressure and temperature are small. For large gradients, the results of the physics-based EOS deviates from Hand's model, and shows it is critical to incorporate these gradients in recovery predictions at large scale.

Low salinity water and polymer flooding in sandstone reservoirs: Upscaling from nano-to macro-scale using the maximum energy barrier

“…Mohammadi and Jerauld (2012) performed several simulations under varying conditions using BP's in-house simulator, VIP; concluding that the use of low-salinity polymer yields an incremental oil recovery in both the secondary and tertiary recovery modes. Khorsandi et al (2016) developed a model to investigate the synergetic effect of LSWF and polymer flooding through cation exchange reactions using an in-house simulator PennSim. The adsorbed Na + concentration was used as the interpolant for wettability alteration.…”

Fundamental Basis for Action of a Chemical Demulsifier Revisited after 30 Years: HLDNas the Primary Criterion for Water-in-Crude Oil Emulsion Breaking

“…This is thus an absolute minimum when scanning any of the five to six independent variables. The pressure was only slightly studied in the first laboratory experiments, mainly because its effect on liquid systems is weak. − …”

Formulation in Surfactant Systems: From-Winsor-to-HLDN

“…This method using two compensating effects produced by a bi-dimensional variation has been carried out in the past 40 years with all variables susceptible to alter the surfactant interactions with oil and water, i.e., salinity, ACN, temperature, pressure, surfactant head, surfactant tail, surfactant intermediate for extended type, co-surfactant type and concentration, pH, and probably others. The corresponding data schematically illustrated in Figure 7 can be found in old [103,104,[167][168][169][159][160][161][162][163][164][165][166] and new articles [170,171,180,[172][173][174][175][176][177][178][179], including a comprehensive review book [78] and recent general articles [2,136,157], which report the numerical value of the coefficients, i.e., the numerical value of the slope of the optimum line indicated in Figure 7 plots. It is worth noting some particularities in these schematic plots.…”