Abstract
In this paper, two methods of kernel bandwidth and wavelet transform are used for simultaneous upscaling of two features of hydrocarbon reservoir. In the bandwidth method, the criterion for upscaling is the cell variability, and by calculating the optimal bandwidth and determining the distance matrix, the upscaling process is performed in a completely non-uniform and unregularly manner. In areas with extreme variability, the bandwidth is considered small enough to maintain the fine scale characteristics of model. Conversely in homogenous areas, with the choice of large bandwidth, the maximum rate of upscaling will occur. The bandwidth upscaling algorithm is an iterative and hierarchical algorithm. The bandwidth method, unlike conventional scale-up methods, focuses on how to upgrid cells and, by determining the optimal averaging window, we will have the least loss information for the fine scale model. Upscaling is a pre-processing to building a simulator model with lower cell number, and thus, reducing volume and computational cost, while maintaining and retaining the basic information of the fine model. Due to the various variability of the reservoir features, the attribute upscaling pattern differs, and in order to show the variability of two features in the upscaling model simultaneously, it is suggested in this paper to upscale two features simultaneously. For simultaneous upscaling, we applied two different approaches; minimum and maximum bandwidth. Moreover, wavelet transformation is applied to upscaling the model. Then, as a result, the variance of the scale-up models based on wavelet is about one-third of the variance of the bandwidth method. Simulation results show that the bandwidth method is a good approach for upscaling the heterogeneous reservoirs.
Highlights
Reservoir simulation plays an important role, and is used to predict and analyze fluid for decisions and management of oil production
Upscaling of reservoir properties based on cell variability is the main idea of scale-up in the kernel function bandwidth method
In areas with high heterogeneity, the selection of small bandwidths will preserve the basic information of the finescale model, and in areas with smooth or small variability, large bandwidths will reduce the number of cells
Summary
Reservoir simulation plays an important role, and is used to predict and analyze fluid for decisions and management of oil production. Models for geological characterizations typically contain many cells. The number of cells is typically an order of 107À1010 cells, which are referred to as fine scale models. These models represent geological variation on fine scales. Upscaling of the geological fine model is required because computational costs can make it unpractical to perform compositional simulations in fine-scale models. Upscaling of fine-scale geological models is important and the use of coarse-scale models in reservoir simulation is necessary to reduce computational time [1]. To develop a reduced-order model from a fine scale model, first, we must create a grid model with reduced spatial
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