A Multi-Objective Optimization Workflow for Steam-Alternating-Solvent Heavy Oil Recovery Process Design

  • Author / Creator
    Coimbra Paniagua, Luis Eduardo
  • The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g. propane) being injected into a horizontal well pair to produce the heavy oil. Companies are interested in these solvent-based methods, as they 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, proper design of the process in heterogeneous reservoirs is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. In this work, a novel hybrid multi-objective optimization (MOO) workflow is developed to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs.
    The construction of the heterogeneous models involves the following steps: first, a set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir; next, sequential indicator simulation is performed to construct heterogeneous models with varying shale proportions and correlation lengths. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis is performed to examine the impacts of shale barriers on SAS production and to formulate a set of operational/decision parameters (e.g. solvent concentration, number/duration of cycles, bottom-hole pressure) and the objective functions (e.g. recovery factor and cumulative solvent injection) to be optimized. The non-dominated sorting genetic algorithm II (NSGA-II), which is a MOO scheme, is applied to search for the optimal sets of decision parameters. To account for multiple reservoir models representing the different realizations of the shale barrier configuration, a weighted objective function, which represents an average measure over all reservoir models, is employed. Finally, to reduce the computational cost, several proxy models are included in the hybrid workflow to evaluate the defined objective functions.
    The growth of a steam-solvent chamber is hampered due to the presence of shale barriers, particularly in the near-well region. These observations are consistent with those reported in several previous studies. However, the behavior of SAS may be different from the SAGD process alone, depending on the relevant solvent transport mechanisms such as dispersion. Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. Integrating the proposed proxy models in a hybrid optimization workflow has considerably reduced the computation requirement.
    This study describes an efficient Pareto-based optimization workflow for the designing of SAS operational parameters; its main advantage is that it can consider multiple conflicting objective functions and different uncertain reservoir heterogeneity scenarios. This work offers a promising potential to de-risk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.

  • Subjects / Keywords
  • Graduation date
    Spring 2020
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
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