Semi-Analytical Modeling of Steam-Solvent Gravity Drainage of Heavy Oil and Bitumen, Part 1: Enhanced Flow Rate at Mobile Zone

Abstract

AbstractWith increasing world demand for energy, more attention has been given to the exploitation of the huge resources present in the form of heavy oil and bitumen. Although thermal methods such as steam assisted gravity drainage (SAGD) are very successful in recovering heavy oil and bitumen, the low thermal efficiency of the process and the high level of greenhouse gas emissions and water usage remain major concerns.Co-injection of solvent with steam has shown to be promising in enhancing oil rates as well as in reduction of energy and water consumption with lower environmental impacts. In hybrid steam-solvent methods, there is a balance between the solubility of the solvent and its ability to reduce bitumen viscosity, and the viscosity reduction due to temperature increase. Therefore, proper selection of the solvent for the operating conditions is key to improving the overall efficiency of the steam- solvent process over the steam-only method.In this study, enhancement of the oil flow rate in the hybrid steam-solvent process is investigated using steady state temperature and unsteady state concentration distribution ahead of the interface with different operational parameters. The Integral Method is employed for prediction of solvent distributions, and the viscosity variation in the mobile zone is determined using the Shu (1984) correlation. The results show that the fractional increase in oil flow rate depends on type and mole fraction of the solvent in the steam chamber. It is also observed that large values of mechanical dispersivity factor enhances oil rate significantly; and drainage rate increases linearly with operating pressure.The results can be used to find the optimum solvent candidate and the injection strategy to maximize the flow rate of the Expanding Solvent SAGD process. This analysis can also be applied for a mixture of solvents based on available experimental data.