Abstract
It is vital to retain the 3D geological model main structure and heterogeneity features in the simulation model. This could be assured only through careful design of the simulation model areal grids; representative reservoir simulation layering system and quality controlled petrophysical data up-scaling process. This paper presents the methodology and the QC tools followed to upscale a 25 million cell geological model into a 0.72 million-cell dynamic simulation model, while maintaing reservoir structure and heterogeneity features.
The layers of simulation models have to be as detailed as possible to capture identified key geological features such as barriers, high permeability streaks, and pinchouts. Mechanistic simulation modeling, along with permeability variance maps, and permeability profile graphs are utilized to check the adequacy of the reservoir layering system. When required, new simulation layers are introduced into the model.
Mechanistic sector simulation is employed to evaluate the appropriate level to which the simulation model areal cell size are coarsened and still maintained the main heterogeneity features of the geological model. The cells are kept as small as possible in order to keep minimum number of grid blocks between existing producers for future infill wells and sidetrack workovers. Effort is made to make sure that most of the grid cells are mapped exactly over the geological model cells to reduce the unescapable errors introduced in the upscaling process of petrophysical data. The cells are oriented in the direction of the main faults of the field, which were modeled as slanted faults. The design maintained the orhogonlity of grid cells sitting next to the fault surfaces.
Upscaling of the model petrophysical data is a very sensitive and crucial process. Different upscaling techniques were applied to insure best practice. QC tools are developed to check the quality of the upscaling process and its adequacy.
A successful application of the structural and petrophysical data upscaling is a key to the construction of a quality simulation model, which is successfully initialized and in the process of history matching.
