Abstract
In comparison to Steam-Assisted Gravity-Drainage (SAGD), the technique of injecting of warm solvent vapor into the formation for heavy oil production offers many advantages, including lower capital and operational costs, reduced water usage, and less greenhouse gas emission. However, to select the optimal operational parameters for this process in heterogeneous reservoirs is non-trivial, as it involves the optimization of multiple distinct objectives including oil production, solvent recovery (efficiency), and solvent-oil ratio. Traditional optimization approaches that aggregate numerous competing objectives into a single weighted objective would often fail to identify the optimal solutions when several objectives are conflicting. This work aims to develop a hybrid optimization framework involving Pareto-based multiple objective optimization (MOO) techniques for the design of warm solvent injection (WSI) operations in heterogeneous reservoirs.
First, a set of synthetic WSI models are constructed based on field data gathered from several typical Athabasca oil sands reservoirs. Dynamic gridding technique is employed to balance the modeling accuracy and simulation time. Effects of reservoir heterogeneities introduced by shale barriers on solvent efficiency are systematically investigated. Next, a state-of-the-art MOO technique, non-dominated sorting genetic algorithm II, is employed to optimize several operational parameters, such as bottomhole pressures, based on multiple design objectives. In order to reduce the computational cost associated with a large number of numerical flow simulations and to improve the overall convergence speed, several proxy models (e.g., response surface methodology and artificial neural network) are integrated into the optimization workflow to evaluate the objective functions.
The study demonstrates the potential impacts of reservoir heterogeneities on the WSI process. Models with different heterogeneity settings are examined. The results reveal that the impacts of shale barriers may be more/less evident under different circumstances. The proxy models can be successfully constructed using a small number of simulations. The implementation of proxy models significantly reduces the modeling time and storages required during the optimization process. The developed workflow is capable of identifying a set of Pareto-optimal operational parameters over a wide range of reservoir and production conditions.
This study offers a computationally-efficient workflow for determining a set of optimum operational parameters relevant to warm solvent injection process. It takes into account the tradeoffs and interactions between multiple competing objectives. Compared with other conventional optimization strategies, the proposed workflow requires fewer costly simulations and facilitates the optimization of multiple objectives simultaneously. The proposed hybrid framework can be extended to optimize operating conditions for other recovery processes.
