identifying flow units using an artificial neural network approach optimized by the imperialist competitive algorithm

the spatial distribution of petrophysical properties within the reservoirs is one of the most importantfactors in reservoir characterization. flow units are the continuous body over a specific reservoirvolume within which the geological and petrophysical properties are the same. accordingly, anaccurate prediction of flow units is a major task to achieve a reliable petrophysical description of areservoir. the aim of this paper was core flow unit determination by using a new intelligent method.flow units were determined and clustered at specific depths of reservoir by using a combination ofartificial neural network (ann) and a metaheuristic optimization algorithm method. at first, artificialneural network (ann) was used to determine flow units from well log data. then, imperialistcompetitive algorithm (ica) was employed to obtain the optimal contribution of ann for a betterflow unit prediction and clustering. available routine core and well log data from a well in one of theiranian oil fields were used for this determination. the data preprocessing was applied for datanormalization and data filtering before these approaches. the results showed that imperialistcompetitive algorithm (ica), as a useful optimization method for reservoir characterization, had abetter performance in flow zone index (fzi) clustering compared with the conventional k-meansclustering method. the results also showed that ica optimized the artificial neural network (ann)and improved the disadvantages of gradient-based back propagation algorithm for a better flow unitdetermination.