1.                 Model Build and Data Distribution: 

·       The pore throat values are first divided into geo-categories (bins). Within each pore-throat category, a value is chosen as the best representative: Bins: 25 to 50. Perform a good model (the number of bins is directly related to the amount of available data). Details are shown in Figure 2.

·       For a specific depth, the fuzzy possibilities should be determined separately for each of the measured logs.

·       Their fuzzy possibilities are combined to calculate the combined fuzzy possibility for a specific bin.

The two highest fuzzy possibilities are taken as the most probable categories for those log measurements. 

2.    Fuzzy Model Results: 

Through this iterative process, pore throats have been defined. On the permeability-porosity cross-plot (Figure 3, right), we can see different clouds, which show sub-parallel trends, resulting in a lees step of the total population, whose clouds are subdivided mainly by permeability value ranges. These pore throat arrangements present, in fact, a quite wide range of porosity but a relatively narrow range of permeability, allowing the rock type corresponding to each family pore throat line (based on Pittman R35 model. The Figure shows the final plot and porosity-permeability match, and on the right side shows the pore throat distribution according to Pittman R35 official equation in the field project.

Parameters applied: Log_R35_comb from cored wells, GR_norm, SXO, PEF_norm, RHOZ, APLC, and RLA1 curves.

The permeability-porosity cross plot for Pittman R35 pore throat ratio can be observed in the figure.