Multi-level delumping strategy for thermal enhanced oil recovery simulations at low pressure

Abstract

We present a multi-level delumping method suitable for thermal enhanced oil recovery processes. At low pressures, the temperature variable is the most critical factor impacting the displacement process through viscosity reduction and evaporation/condensation effects. Hydrocarbon components are vaporized under high temperatures, move downstream in the gas phase and condense back to the liquid phase. That process is governed by the K-values of the components, evaporating out of the liquid phase sequentially with increasing temperatures. To reduce the computational cost, it is standard practice to reduce the number of (pseudo-) 6 components used in thermal reservoir simulation. Depending on the number and type of hydrocarbon pseudo-components retained in the simulations, we may not be able to capture the correct displacement due to large errors in the lumped phase behavior (flash) computations. We address that problem through a multi-level method: we use data obtained from a short simulation using the most detailed fluid description available, and leverage that information to guide a delumping process. We use temperature as a proxy variable for composition, and select reference temperatures. We extract the corresponding reference compositions from the detailed run and use them to extend the lumped pseudo-components to an approximate detailed composition. We compute the phase mole fractions as well as the gas compressibility factor. We test our method using six heavy oil samples, and under two different recovery processes: hot nitrogen injection and in-situ combustion (air injection and exothermic oxidation reactions). The average error on the liquid mole fraction is reduced by 4–12 times (depending on the oil samples) compared to the flash using pseudo-components, and the maximum error by 6–48 times. We illustrate that the method is amenable to manually adding more information about the physics of some oil samples. We also discuss how to efficiently pick the reference temperatures. For uniformly sampled temperatures (between a minimum and maximum temperature), we conduct a sensitivity study which led us to use six temperatures. We ran both local (Pattern Search, PS) and global (Particle Swarm Optimization, PSO) gradient-free optimization methods. PS is able to find the closest local minimum to the uniform set, giving a limited improvement of 6.5%. The known increased cost for PSO is worth the investment in at least one of the cases we considered, leading to a 67% improvement.