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Parameterization of Channelized Training Images: A Novel Approach for Multiple-Point Simulations of Fluvial Reservoirs

Fadlelmula, Mohamed M.
Akın, Serhat
Duzgun, Sebnem
Aiming at analyzing the impact of Training Image (TI) uncertainty on simulated reservoir models; this study presents a novel approach for parameterizing channelized TIs. First, the channel structure is represented mathematically in two dimensions (2D) with a Sine function. Then, the parameters of the function (i.e. amplitude and phase) and the number of channels are modified to generate different 2D TIs. Next, the third dimension (Z-direction) slices are added to generate three dimensional TIs. Thus, a TI becomes a function of four parameters, namely, the number of channels, the number of waves in each channel which is controlled by the phase value, the amplitude value of waves, and the number of Z-direction slices. The generated TIs are then used to simulate a synthetic reservoir model utilizing a proposed MPS methodology. Analysis of the generated models shows that, the reservoir model is sensitive to the number of channels, number of waves and number of Z-direction slices in the TI used.