Upscaling of the geomechanics parameters of reservoir using the kernel function method with adaptive bandwidth and comparing with the results of wavelet transformation

In this paper we are used as two different approaches; wavelet transformation and adaptive bandwidth in kernel method in upscaling process of geomechanical parameters of the reservoir. Geomechanics in oil field have been investigated compressive strength parameters, Young's Moduli, Bulk Moduli and shear Moduli to determine the quality of reservoir and rock as well as the effect of rock resistance and stress on the behavior of formations as a result of oil activities. The geomechanical parameters of the reservoir rock are calculated using petrophysical logs such as acoustic and porosity log. Identifying uniform zones and classifying rock quality requires looking at geomechanical parameters along a well. Upscaling can be used to ease the use of this classifier. In the wavelet theory, after the analyze or the desired signal to the desired level, the upscaled signal will be obtained from the composition of the approximation section of the same level and the remaining samples of the coefficient of detail. This is the same as multiresolution upscaling. In upscaling using the bandwidth of the kernel function, the threshold or bandwidth is defined which is in fact a function of the geomechanical parameter variability. Adaptive bandwidth method can provide a good model upscaling of cells. In areas of high variability, by choosing optimal bandwidth, the cells remain fine, and vice versa, in areas with smooth changes, the number of cells will be merged more together. Comparison of the results of the two methods is observed. Under identical conditions, the upscaling error of the upscaled-optimized model with the kernel bandwidth method is about 1.4 wavelet transforms, and it is also possible that according to the probable error rate, depending on the threshold and appropriate bandwidth can be used to determine the number of upscaled block of the simulated model according to the computational time.