Reservoir simulation models that represent large-sized reservoirs require significant use of High-Performance Computing resources. Several solutions aim to reduce the load of an individual simulation such as upscaling to consequently reduce the number of grid cells of the model, splitting the model into independent sub-models using crop functionality, or dividing the model into sectors and running these sub-models independently. As it preserves the flux boundary condition, sector modeling could potentially result in the least erroneous solution, especially if the sectors are defined on the regions of least connectivity. This paper aims to propose an automated, intelligent method to divide the reservoir model into an arbitrary number of least-connected smaller sectors. Streamline simulation output is used as a representation of reservoir connectivity. A graph is built using cells as graph vertices, and the edge weight is calculated based on the time of flight of oil and water between cell pairs. By presenting the problem in this manner, a graph is built where the non-water cells have equal weight, and the stronger the connection between two cells, represented by the lower time of flight, the larger the edge weight. A graph partitioning tool is used to minimize the total weight of edges cut while keeping the number of vertices in each sub-graph balanced up to a specified tolerance. Partitioning of a graph specified this way is equivalent to splitting the reservoir model into sub-models while avoiding cutting of strongly connected parts, hence minimizing the boundary flux. Applying this method to sector modeling allows splitting a model into a number of smaller sub-models that can be used independently as the interactions between them are minimized, as demonstrated by minimizing flux between them. The proposed novel method has been tested on several real-field as well as synthetic reservoir models. The method has resulted in sub-models of loosely connected reservoirs. The advantage of our proposed method is especially seen for strongly connected models where it is difficult to identify the least erroneous partitioning for sector generation manually. With the use of our method, it is guaranteed to automatically find the least connection, while minimizing the error that sector division produces, as evident by the low flux between the sector models.