model based interpretation methodology for evaluating carbonate reservoirs u201d society of petroleum engineers spe 71704 spe annual technical conference and exhibition new orleans september 2001 in workflow 700 decision step 701 asks whether or not cores are available if cores are available the flow proceeds to 703 micp availability if micp data are available the micp testing process provides comprehensive data to characterize the pore system linked to its dynamic performance through its pore throat size distribution micp derived pore types ipt have to be combined with larger scale observations such as vugs and fractures this information is provided by specialty logs the next step is micp digital core data grouping step 711 clustering data from the full range of capillary pressure provides a way to include full scale of porosity from nanopores to macropores and account for multimodal systems micp grouping can be done using statistical clustering techniques or neural networks the resulting groups are named pore type groups ptgs ptgs can be also obtained from digital core 3d data with appropriate integration of multi scale volumes still referring to fig1 the micp branch of the workflow then moves to prediction of ptg from core pks data 713 ptgs are defined only for micp or digital core samples which usually have a limited number of samples the ability to make a reliable prediction of ptgs from pks data can increase the pore type database by an order of magnitude prediction of ptgs from core pks data can be done by multivariate statistical methods such as stepwise discriminant analysis sda or neural networks typical predictive variables are winland parameter r35 log k phie effective porosity and sqrt k phie the resulting ptg_pred is used to define ptg predictive algorithms using well logs pore type groups are then also.