Low-Tension Gas Modeling in Surfactant Alternating Gas and Surfactant/Gas Coinjection Processes

Abstract

Abstract In this paper, we present a framework to model low-tension gas flood process and implement the model into the UT in-house compositional gas reservoir simulator (UT-DOECO2). A gas compositional model is coupled with microemulsion phase behavior to capture important mechanisms in hybrid gas-chemical flood processes in porous media. Two different surfactant molecules are simultaneously applied: one to lower interfacial tension to ultra-low values and one to keep foam stable as gas mobility control agent. This process cannot currently be modeled using the commercial reservoir simulators. We implemented the option for two surfactants into the existing gas compositional simulator with foam and hysteresis options. A predictive simulator would make it possible to select the best candidates for field application and tailor process design to particular characteristics of each field. In the field-scale application of the Surfactant Alternating Gas (SAG) process, multiphase fluid behavior in porous media is modeled using three-phase compositional relative permeability and three-phase hysteresis models to include both compositional and saturation history effects. These models represent a more-accurate prediction of the cycle-dependent properties of SAG. The gas entrapment in the foam flow is used to predict the hysteresis effect within each cycle using a dynamic Land coefficient. The in-situ foaming behavior is estimated based on the mechanistic foam models. This study, further, evaluates mobilization and displacement of residual oil in tight reservoirs using the low-tension gas flood and compares the results with other EOR options. Using a reliable multiphase simulator low-tension gas experiments can be scaled up to the field and to optimize chemical-gas EOR process design. Numerical simulation of the SAG with and without hysteresis is used to assess the effect of the gas-entrapment on oil recovery and gas utilization factor in a field-scale application.