One of the key steps toward improving the predictability of air‐injection‐based processes relies on the development of accurate phase‐behavior models of the oil.

Historically, for in‐situ combustion (ISC) in heavy oils and bitumens, phase behavior was often ignored because the physical aspects of the process (e.g., distillation) were not considered to be as significant as the oxidation reactions. However, this step is important for several reasons. First, the compositional model should reflect the phase behavior of the original fluids. Second, reaction rates are dependent on the concentrations of the reactants, which in turn are affected by the volatility of the components. This is particularly important for lighter oils (but not unimportant for heavier oils), where the phase equilibrium between the liquid and vapor can have a significant effect on the flammability range for vapor‐phase combustion at given temperature and pressure conditions. Finally, for the case of lighter oils, a good phase‐behavior model is required to capture the compositional effects of the resulting flue‐gas drive.

This study presents a practical work flow to develop a phase‐behavior model in terms of saturates/aromatics/resins/asphaltenes (SARA) fractions that is aligned with the reaction‐modeling approach used in most kinetic models. The methodology requires conventional‐oil‐characterization (i.e., dependent on distillation cuts) and conventional‐phase‐behavior experiments (e.g., differential liberation), as well as oil characterization in terms of SARA fractions.

The first step of the method consists of splitting the heaviest oil fraction (i.e., plus fraction), followed by lumping all of the single‐carbon‐number (SCN) components, in such a way that the new oil characterization honors the SARA data available, such as composition and the physical properties of each fraction (e.g., molecular weight). In addition, the gas components (e.g., methane) would be treated as additional components as necessary. The second step is to tune an equation of state (EOS), in terms of the SARA‐based model, to match the relevant laboratory experiments. Finally, the tuned EOS would be used to export the equilibrium constants (K‐value tables) to the thermal numerical simulator.

Different examples on the application of the phase‐behavior‐modeling work flow are presented and discussed in detail for heavy and light oils. This work opens up opportunities to model the ISC process for any oil (i.e., light or heavy) using the currently available kinetic models, which in turn is an important step toward improving the predictability of ISC processes using reservoir simulation.

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