The construction of facies model is one of the most crucial undergone procedures of any stochastic geomodeling. It could be employed as a conditional data for any property simulation which results in a more reliable reservoir characterization in further steps.

A section of an Iranian gas reservoir with six wells was studied to determine the 3D reservoir facies model. Fifteen reservoir facies were first detected along one of the wells with detailed core and thin section descriptions. Due to the significant difference between the core and log data resolution, facies were clustered into four major groups regarding the digenetic processes and petrophyiscal lithofacies properties (permeability and porosity). The lithofacies specification effect on petrophysical properties distribution is usually the only criterion that is considered in conventional simulations. In this paper, however, the diagenetic processes which have a main influence on reservoir properties variation, especially permeability and porosity, have been considered in reservoir facies clustering.

After clustering process, using the available logs and with the help of the neural network, facies distributions along other five wells were also specified. To generate a 3D facies distribution into reservoir scale, SIS methodology was utilized with reference to the previously generated reservoir zonation from sequence stratighraphy, where Acoustic Impedance seismic attribute was set as the secondary data. This attribute was produced by model based inversion method applied in seismic cubes.

Since the diagenetic processes have highly influenced the rock properties of this carbonate reservoir, their effects have been considered as the most important parameter in reservoir facies determination. The ultimate model properly honored each expected sequence cycle properties such as diagenetic processes and lithofacies.

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