|تعداد مشاهده مقاله||103,459,266|
|تعداد دریافت فایل اصل مقاله||81,431,979|
Two-dimensional upscaling of reservoir data using adaptive bandwidth in the kernel function
|International Journal of Mining and Geo-Engineering|
|مقاله 5، دوره 54، شماره 2، اسفند 2020، صفحه 123-128 اصل مقاله (831.49 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/ijmge.2019.270774.594768|
|Mohammadreza Azad 1؛ Abulghasem Kamkar Ruhani2؛ Behzad Tokhmechi2؛ Mohammad Arashi3|
|1Phd Student of Shahrood university of Technology|
|2Faculty of Mining, Petroleum & Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran|
|3Faculty of Mathematical Science, Shahrood University of Technology, Shahrood, Iran|
|In this paper, a new method called adaptive bandwidth in the kernel function has been used for two-dimensional upscaling of reservoir data. Bandwidth in the kernel can be considered as a variability parameter in porous media. Given that the variability of the reservoir characteristics depends on the complexity of the system, either in terms of geological structure or the specific feature distribution, variations must be considered differently for upscaling from a fine model to a coarse one. The upscaling algorithm, introduced in this paper, is based on the kernel function bandwidth, written in combination with the A* search algorithm and the first-depth search algorithm. In this algorithm, each cell in its x and y neighborhoods as well as the optimal bandwidth, obtained in two directions will be able to be merged with its adjacent cells. The upscaling process is performed on artificial data with 30×30 grid dimensions and SPE-10 model as real data. Four modes are used to start the point of upscaling and the process is performed according to the desired pattern, and in each case, the upscaling error and the number of final upscaled blocks are obtained. Based on the number of coarsen cells as well as the upscaling error, the first pattern is selected as the optimal pattern for synthetic data and the second pattern is selected as the optimal simulator model for real data. In this model, the number of cells was 236 and 3600, and the upscaling errors for synthetic and real data were 0.4183 and 12.2, respectively. The results of the upscaling in the real data were compared with the normalization method and showed that the upscaling error of the normalization method was 15 times the upscaling error of the kernel bandwidth algorithm.|
|Upscaling؛ Bandwidth؛ Kernel؛ Cell؛ Optimum model|
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