• Energy Research
• 7. Clean energy close
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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.

Vapex (vapor extraction) is a nonthermal process that has significant potential in providing a more environmentally friendly and energy-efficient alternative to steam injection. Vaporized solvent injected in-situ dissolves into the oil and reduces oil viscosity, allowing the oil to flow to a horizontal production well via gravitational forces. While compositional simulators are available for assessing the Vapex performance, the simulation process may become difficult when taking into account the uncertainty due to reservoir heterogeneity. A semi-analytical proxy is proposed to model the process, in a way analogous to the steam-assisted gravity drainage (SAGD) model described by Butler, who demonstrated the similarity between two processes with a series of Hele-Shaw experiments and derived an analytical steady-state flow rate relationship that is comparable with the SAGD case. Solvent concentration and intrinsic diffusivity are introduced in this model instead of temperature and thermal diffusivity in SAGD. In this paper, analytical solutions and implementation details for the Vapex proxy are presented. The proposed approach is then applied to various reservoirs discretized with spatially varying rock porosity and permeability values; bitumen drainage rate and solvent penetration are calculated sequentially at grid blocks along the solvent–bitumen interface over incremental time steps. Results from this model are compared against experimental data available in the literature as well as detailed compositional simulation studies. Computational requirement of the proxy in comparison with numerical simulations is also emphasized. An important contribution from this work is that process physics are built directly into this proxy, giving it an advantage over other data-driven modeling approaches (e.g., regression). It can be used as an efficient alternative to expensive detailed flow simulations. It presents an important potential for assessing the uncertainty due to multiscale heterogeneity on effective mass transfer and the resulting recovery performance, as well as assisting decisions-making for future pilot and field development.