The Cloud transform reproduces the conditional distributions of a dependent 3D parameter given an independent 3D parameter (Bashore et al. 1994). This distribution is estimated empirically from well data and the secondary variable. The estimated distribution will then be the basis for the cloud transform. This works in the same way as the Normal score transform except that the CDF being used is a 2D CDF estimated empirically from well data and the independent 3D parameter. This allows specifying a model that reproduces the scatter plot from the wells in a 3D volume. In a case study from offshore Abu Dhabi, the relationship between porosity and permeability is found to be non-linear and Cloud transform technique was applied for permeability distribution. The porosity, which is populated using Gaussian Simulation used as independent 3D parameter and an empirical relationship was derived between porosity (3D parameter) and log derived permeability (calibrated to core at well location for each rock type, which was used to estimate permeability distribution for the Cloud transform. The initial simulation results show positive results where good history match was reached without applying multiplier for the producing wells.


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