Abstract
Solvent injection alone, or in combination with steam, is currently receiving considerable attention as an emerging in-situ oil sands technology in Alberta. The goal of solvent and steam assisted recovery processes is to reduce energy consumption and greenhouse gas emissions over the use of steam alone. Pilot studies in the field indicate a significant difference between reservoir simulation predictions, and field performance and previous studies have demonstrated that the assumption of instantaneous equilibrium is not valid for solvent-bitumen interactions and can introduce significant inaccuracies to the modelling results. In addition to assuming non-equilibrium, this study seeks to determine the mechanism of solvent dissolution in bitumen and the expected improvement in oil recovery, if any, when a solvent is injected with steam.
The unique aspect of this work is that instantaneous phase equilibrium is not assumed, as is typical of numerical simulations for solvent-steam applications. Partial equilibrium was not based on an empirical factor or concept. Rather, this study bases phase equilibrium on an analytical model of the dissolution and mobilization of a drop of bitumen inside a pore, by solvent and heat. The analytical solution indicates that the time required for a drop of bitumen to mobilize towards the production well is at least three times greater for solvent via diffusion and dispersion than by heat conduction. The analytical solution was developed for solvent injection, heat injection, and co-injection for several boundary conditions.
The single drop model is built into a new thermal compositional simulator developed for this study. Thermal solvent injection processes were investigated for non-equilibrium phase behaviour. The results were compared with the case of instantaneous equilibrium, showing the reason for the previous lack of accurate predictions for solvent injection in oil sands reservoirs.
The results and extensions of this work will be of interest in heavy oil production because they serve to explain the unexpected performance and frequent lack of success of these processes. This model is capable of precisely predicting solvent injection concentration, flow rate, and recovery at the field scale, making it possible to determine whether or not solvent injection is appropriate in any given situation.
