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Summary Numerous steam-assisted gravity-drainage (SAGD) optimization studies published in the literature combined numerical simulation with graphical or analytical techniques for design and performance evaluation. Efforts have integrated the simulation exercise with global optimization algorithms. Some studies focused on optimization of cumulative steam/oil ratio (cSOR) in SAGD by altering steam-injection rates, while others focused on optimization of net cumulative energy/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 (NPV) 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, applications of hybrid GA 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 coinjected with steam. The objective function, defined on the basis of cSOR and recovery factor, was optimized by changing injection pressures, production pressures, and injected solvent/steam ratio. Techniques, including orthogonal arrays (OA) for experimental design (e.g., Taguchi's arrays) and proxy models for objective-function (F) 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 central-processing-unit time (e.g., fewer number of iterations) compared with 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.

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Heavy oil and bitumen recovery cost is excessive mainly due to high energy requirement to generate heat and its environmental impacts. Steam Assisted Gravity Drainage (SAGD) is an example of this case; the determination of optimal operating conditions, such as injection rates and well locations, based on reservoir and fluid characteristics is essential in the design of field applications.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 limited 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. Typical scoring functions were the net present value per hectare of land (NPV/ha) by controlling steam and solvent rates. Several studies also considered total project net present value calculation by changing total project area, capital cost intensities, solvent prices, discount rate, 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, applications of hybrid genetic algorithm were rarely found.In continuation of these efforts, we focused on optimizing the SAGD process and its extension to solvent-additive SAGD using hybrid genetic algorithm. The objective function was defined to obtain the lowest cumulative steam-oil ratio (cSOR) and highest recovery factor. It was used later as scoring function by changing operating pressure, solvent-to-steam ratio, and steam injection rates. The results in this paper can be implemented directly in the efforts of minimization of cost and