Design of warm solvent injection processes for heterogeneous heavy oil reservoirs: A hybrid workflow of multi-objective optimization and proxy models

Abstract

Injection of warm solvent vapor into the formation is often considered as a cleaner alternative for heavy oil production. However, the design of relevant operational conditions is challenging, as it involves the optimization of multiple distinct objectives including oil production and solvent-oil-ratio (solvent usage efficiency). This work develops 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 models are constructed based on field data gathered from several typical Athabasca oil sands reservoirs. Models with different heterogeneity settings and the impact of solution gas on WSI production are examined. Next, non-dominated sorting genetic algorithm II (NSGA-II), is employed to optimize two operational parameters, i.e., bottom-hole pressures, based on multiple design objectives. Several proxy models are integrated into the optimization workflow to evaluate the objective functions at reduced computational costs. The performance of the proposed workflow is validated via both homogeneous and heterogeneous cases, and it is demonstrated that a set of Pareto-optimal operating conditions for different reservoir settings can be obtained. The results reveal that the optimal bottom-hole pressure of the producer should be kept at a minimum, while there is more flexibility in the injection bottom-hole pressure. Compared with other conventional optimization strategies, the proposed workflow requires fewer costly simulations and facilitates the optimization of multiple objectives simultaneously. The result demonstrated a great potential for extending the developed MOO framework to optimize operational conditions for other natural resource extraction processes.