Design of Steam Alternating Solvent Process Operational Parameters Considering Shale Heterogeneity

Ma, Zhiwei (University of Alberta, Canada, and Los Alamos National Laboratory) | Coimbra, Luis (University of Alberta, Canada) | Leung, Juliana Y. (University of Alberta, Canada (Corresponding author))

OnePetro 

Summary The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g., propane) injected into a horizontal well pair to produce heavy oil. These solvent-based methods entail a smaller environmental footprint with reduced water usage and greenhouse gas emissions. However, the lack of understanding regarding the influences of reservoir heterogeneities, such as shale barriers, remains a significant risk for field-scale predictions. Additionally, the proper design of the process is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. This work develops a novel hybrid multiobjective optimization (MOO) workflow to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs. A set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir. Next, multiple heterogeneous models (realizations) are built to incorporate complex shale heterogeneities. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis examines the impacts of shale barriers on SAS production. It is used to formulate a set of operational/decision parameters (i.e., solvent concentration and duration of solvent injection cycles) and the objective functions (cumulative steam/oil ratio and propane retention). The nondominated sorting genetic algorithm II (NSGA-II) is applied to search for the optimal decision parameters. Different formulations of an aggregated objective function, including average, minimum, and maximum, are used to capture the variability in objectives among the multiple realizations of the reservoir model. Finally, several proxy models are included in the hybrid workflow to evaluate the defined objective functions to reduce the computational cost. Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. It is recommended to inject solvent for longer periods during both the early and late SAS stages. It is also noted that cases with higher objective function values are observed with more heterogeneities. This work offers promising potential to derisk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.

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