Many Steam Assisted Gravity Drainage (SAGD) optimization studies published in the literature combined numerical simulation with graphical or analytical techniques for design and performance evaluation. There have been numerous efforts that integrated the simulation exercise with global optimization algorithms. Some studies focused on optimization of cumulative steam-to-oil ratio (cSOR) in SAGD by altering steam injection rates, while others focused on optimization of cumulative net energy-to-oil ratio (cEOR) in solvent-additive SAGD by altering injection pressures and fraction of solvent in the injection stream. Several studies also considered total project net present value calculation by changing total project area, capital cost intensities, solvent prices, and risk factors to determine the well spacing and drilling schedule. Optimization techniques commonly used in those studies were scattered search, simulated annealing, and genetic algorithm (GA). However, the applications of hybrid genetic algorithm were rarely found.
In this paper, we focused on optimization of solvent-assisted SAGD using various GA implementations. In our models, hexane was selected to be co-injected with steam. The objective function, defined based on cumulative steam-oil ratio (cSOR) and recovery factor, was optimized by changing injection pressures, production pressures, and injected solvent-to-steam ratio. Techniques including orthogonal arrays (OA) for experimental design (e.g. Taguchi’s arrays) and proxy models for objective function evaluations were incorporated with the GA method to improve computational and convergence efficiency. Results from these hybrid approaches revealed that an optimized solution could be achieved with less CPU time (e.g. fewer number of iterations) compared to the conventional GA method. Sensitivity analysis was also conducted on the choice of proxy model to study the robustness of the proposed methods.
To investigate the effects of heterogeneity in the design process, optimization of solvent-assisted SAGD was performed on various synthetic heterogeneous reservoir models of porosity, permeability, and shale distributions. Our results highlight the potential application of the proposed techniques in other solvent-enhanced heavy oil recovery processes.